Additional Figures
Accompanying ‘Plaque expression levels of HDAC9 in association with plaque vulnerability traits and secondary vascular events in patients undergoing carotid endarterectomy, an analysis in the Athero-EXPRESS Biobank.’
Sander W. van der Laan, PhD | s.w.vanderlaan@gmail.com.
2023-05-31
General Setup
Create a new analysis directory...
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[1] "/Users/slaan3/git/CirculatoryHealth/AE_20211201_YAW_SWVANDERLAAN_HDAC9"
[1] "_archived" "1. AE_20211201_YAW_SWVANDERLAAN_HDAC9.nb.html"
[3] "1. AE_20211201_YAW_SWVANDERLAAN_HDAC9.Rmd" "1. AEDB.CEA.baseline.nb.html"
[5] "1. AEDB.CEA.baseline.Rmd" "2. SNP_analyses.nb.html"
[7] "2. SNP_analyses.Rmd" "20220319.HDAC9.AEDB.CEA.baseline.RData"
[9] "20220319.HDAC9.AESCRNA.results.RData" "20220319.HDAC9.bulkRNAseq.additional_figures.RData"
[11] "20220319.HDAC9.bulkRNAseq.main_analysis.RData" "20220319.HDAC9.bulkRNAseq.preparation.RData"
[13] "20230301.HDAC9.bulkRNAseq.additional_figures.RData" "20230511.HDAC9.bulkRNAseq.additional_figures.RData"
[15] "20230531.HDAC9.bulkRNAseq.additional_figures.RData" "3.1 bulkRNAseq.preparation.nb.html"
[17] "3.1 bulkRNAseq.preparation.Rmd" "3.2 bulkRNAseq.main_analysis.nb.html"
[19] "3.2 bulkRNAseq.main_analysis.Rmd" "3.3 bulkRNAseq.additional_figures.nb.html"
[21] "3.3 bulkRNAseq.additional_figures.Rmd" "4. scRNAseq.nb.html"
[23] "4. scRNAseq.Rmd" "AE_20211201_YAW_SWVANDERLAAN_HDAC9.Rproj"
[25] "AnalysisPlan" "HDAC9"
[27] "images" "LICENSE"
[29] "README.html" "README.md"
[31] "references.bib" "renv"
[33] "renv.lock" "scripts"
[35] "SNP" source(paste0(PROJECT_loc, "/scripts/functions.R"))install.packages.auto("readr")
install.packages.auto("optparse")
install.packages.auto("tools")
install.packages.auto("dplyr")
install.packages.auto("tidyr")
install.packages.auto("naniar")
# To get 'data.table' with 'fwrite' to be able to directly write gzipped-files
# Ref: https://stackoverflow.com/questions/42788401/is-possible-to-use-fwrite-from-data-table-with-gzfile
# install.packages("data.table", repos = "https://Rdatatable.gitlab.io/data.table")
library(data.table)
install.packages.auto("tidyverse")
install.packages.auto("knitr")
install.packages.auto("DT")
install.packages.auto("MASS")
# install.packages.auto("Seurat") # latest version
# Install the devtools package from Hadley Wickham
install.packages.auto('devtools')
install.packages.auto("haven")
install.packages.auto("sjlabelled")
install.packages.auto("sjPlot")
install.packages.auto("labelled")
install.packages.auto("tableone")
install.packages.auto("ggpubr")Background
This notebook contains additional figures of the project “Plaque expression levels of HDAC9 in association with plaque vulnerability traits and secondary vascular events in patients undergoing carotid endarterectomy: an analysis in the Athero-EXPRESS Biobank.”.
Loading data
# load(paste0(PROJECT_loc, "/",Today,".",PROJECTNAME,".bulkRNAseq.main_analysis.RData"))
load(paste0(PROJECT_loc, "/20220319.",PROJECTNAME,".bulkRNAseq.main_analysis.RData"))Fix some variables
We need to get the ‘conventional unit’ versions of cholesterols.
AERNASE.clin.hdac9 <- merge(AERNASE.clin.hdac9,
subset(AEDB.CEA, select = c("STUDY_NUMBER",
"risk614",
"LDL_finalCU", "HDL_finalCU", "TC_finalCU", "TG_finalCU")),
by.x = "STUDY_NUMBER", by.y = "STUDY_NUMBER", sort = TRUE, all.x = TRUE)Additional figures
Age and sex
We want to create per-age-group figures median ± interquartile range.
- Box and Whisker plot for target(s) plaque levels by sex.
- Box and Whisker plot for target(s) plaque levels by (sex and) age group (<55, 55-64, 65-74, 75-84, 85+).
# ?ggpubr::ggboxplot()
compare_means(HDAC9 ~ Gender, data = AERNASE.clin.hdac9, method = "kruskal.test")ggpubr::ggboxplot(AERNASE.clin.hdac9,
x = c("Gender"),
y = "HDAC9",
xlab = "gender",
ylab = "HDAC9 (normalized expression)",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "jitter") +
stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".plaque.Gender.pdf"), plot = last_plot())Saving 12 x 8 in imagelibrary(dplyr)
AERNASE.clin.hdac9 <- AERNASE.clin.hdac9 %>% dplyr::mutate(AgeGroup = factor(case_when(Age < 55 ~ "<55",
Age >= 55 & Age <= 64 ~ "55-64",
Age >= 65 & Age <= 74 ~ "65-74",
Age >= 75 & Age <= 84 ~ "75-84",
Age >= 85 ~ "85+")))
AERNASE.clin.hdac9 <- AERNASE.clin.hdac9 %>% dplyr::mutate(AgeGroupSex = factor(case_when(Age < 55 & Gender == "male" ~ "<55 males" ,
Age >= 55 & Age <= 64 & Gender == "male"~ "55-64 males",
Age >= 65 & Age <= 74 & Gender == "male"~ "65-74 males",
Age >= 75 & Age <= 84 & Gender == "male"~ "75-84 males",
Age >= 85 & Gender == "male"~ "85+ males",
Age < 55 & Gender == "female" ~ "<55 females" ,
Age >= 55 & Age <= 64 & Gender == "female"~ "55-64 females ",
Age >= 65 & Age <= 74 & Gender == "female"~ "65-74 females",
Age >= 75 & Age <= 84 & Gender == "female"~ "75-84 females",
Age >= 85 & Gender == "female"~ "85+ females")))
table(AERNASE.clin.hdac9$AgeGroup, AERNASE.clin.hdac9$Gender)
female male
<55 11 27
55-64 43 124
65-74 58 191
75-84 37 119
85+ 4 9table(AERNASE.clin.hdac9$AgeGroupSex)
<55 females <55 males 55-64 females 55-64 males 65-74 females 65-74 males 75-84 females 75-84 males 85+ females
11 27 43 124 58 191 37 119 4
85+ males
9 Now we can draw some graphs of plaque target(s) levels per sex and age group as median ± interquartile range.
# ?ggpubr::ggboxplot()
compare_means(HDAC9 ~ AgeGroup, data = AERNASE.clin.hdac9, method = "kruskal.test")ggpubr::ggboxplot(AERNASE.clin.hdac9,
x = c("AgeGroup"),
y = "HDAC9",
xlab = "Age groups (years)",
ylab = "HDAC9 (normalized expression)",
color = "AgeGroup",
palette = "npg",
# add = "median_iqr")
add = c("median_iqr", "jitter")) +
stat_compare_means(aes(group = AgeGroup), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".plaque.AgeGroup.pdf"), plot = last_plot())Saving 12 x 8 in imagecompare_means(HDAC9 ~ AgeGroup, group.by = "Gender", data = AERNASE.clin.hdac9, method = "kruskal.test")ggpubr::ggboxplot(AERNASE.clin.hdac9,
x = c("AgeGroup"),
y = "HDAC9",
xlab = "Age groups (years) per gender",
ylab = "HDAC9 (normalized expression",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
# add = "median_iqr")
add = c("median_iqr", "jitter")) +
stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".plaque.AgeGroup_perGender.pdf"), plot = last_plot())Saving 12 x 8 in imageHypertension & blood pressure
We want to create figures of target(s) levels stratified by hypertension/blood pressure, and use of anti-hypertensive drugs.
- Box and Whisker plot for target(s) plaque levels by hypertension group (no, yes)
- Box and Whisker plot for target(s) plaque levels by systolic blood pressure group (<120, 120-139, 140-159,160+)
library(dplyr)
AERNASE.clin.hdac9 <- AERNASE.clin.hdac9 %>% mutate(SBPGroup = factor(case_when(systolic < 120 ~ "<120",
systolic >= 120 & systolic <= 139 ~ "120-139",
systolic >= 140 & systolic <= 159 ~ "140-159",
systolic >= 160 ~ "160+")))
table(AERNASE.clin.hdac9$SBPGroup, AERNASE.clin.hdac9$Gender)
female male
<120 7 22
120-139 30 81
140-159 36 120
160+ 62 169Now we can draw some graphs of plaque target(s) levels per sex and hypertension/blood pressure group as median ± interquartile range.
compare_means(HDAC9 ~ SBPGroup, data = AERNASE.clin.hdac9 %>% filter(!is.na(SBPGroup)), method = "kruskal.test")ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(SBPGroup)),
x = c("SBPGroup"),
y = "HDAC9",
xlab = "Systolic blood pressure (mmHg)",
ylab = "HDAC9 (normalized expression)",
color = "SBPGroup",
palette = "npg",
add = "jitter") +
stat_compare_means(aes(group = SBPGroup), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".plaque.SBPGroup.pdf"), plot = last_plot())Saving 12 x 8 in imagecompare_means(HDAC9 ~ Hypertension.selfreport, data = AERNASE.clin.hdac9 %>% filter(!is.na(Hypertension.selfreport)), method = "kruskal.test")ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(Hypertension.selfreport)),
x = c("Hypertension.selfreport"),
y = "HDAC9",
xlab = "Self-reported hypertension",
ylab = "HDAC9 (normalized expression)",
color = "Hypertension.selfreport",
palette = "npg",
add = "jitter") +
stat_compare_means(aes(group = Hypertension.selfreport), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".plaque.Hypertension.pdf"), plot = last_plot())Saving 12 x 8 in imagecompare_means(HDAC9 ~ Hypertension.drugs, data = AERNASE.clin.hdac9 %>% filter(!is.na(Hypertension.drugs)), method = "kruskal.test")ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(Hypertension.drugs)),
x = c("Hypertension.drugs"),
y = "HDAC9",
xlab = "Hypertension medication use",
ylab = "HDAC9 (normalized expression)",
color = "Hypertension.drugs",
palette = "npg",
add = "jitter") +
stat_compare_means(aes(group = Hypertension.drugs), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".plaque.HypertensionDrugs.pdf"), plot = last_plot())Saving 12 x 8 in imagecompare_means(HDAC9 ~ SBPGroup, group.by = "Gender", data = AERNASE.clin.hdac9 %>% filter(!is.na(SBPGroup)), method = "kruskal.test")ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(SBPGroup)),
x = c("SBPGroup"),
y = "HDAC9",
xlab = "Systolic blood pressure (mmHg) per gender",
ylab = "HDAC9 (normalized expression)",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "jitter") +
stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".plaque.SBPGroup_byGender.pdf"), plot = last_plot())Saving 12 x 8 in imagecompare_means(HDAC9 ~ Hypertension.selfreport, group.by = "Gender", data = AERNASE.clin.hdac9 %>% filter(!is.na(Hypertension.selfreport)), method = "kruskal.test")ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(Hypertension.selfreport)),
x = c("Hypertension.selfreport"),
y = "HDAC9",
xlab = "Self-reported hypertension per gender",
ylab = "HDAC9 (normalized expression)",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "jitter") +
stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".plaque.Hypertension_byGender.pdf"), plot = last_plot())Saving 12 x 8 in imagecompare_means(HDAC9 ~ Hypertension.drugs, group.by = "Gender", data = AERNASE.clin.hdac9 %>% filter(!is.na(Hypertension.drugs)), method = "kruskal.test")ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(Hypertension.drugs)),
x = c("Hypertension.drugs"),
y = "HDAC9",
xlab = "Hypertension medication use per gender",
ylab = "HDAC9 (normalized expression)",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "jitter") +
stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".plaque.Hypertension.drugs_byGender.pdf"), plot = last_plot())Saving 12 x 8 in imagecompare_means(HDAC9 ~ SBPGroup, group.by = "Hypertension.drugs", data = AERNASE.clin.hdac9 %>% filter(!is.na(SBPGroup) & !is.na(Hypertension.drugs)), method = "kruskal.test")ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(SBPGroup) & !is.na(Hypertension.drugs)),
x = c("SBPGroup"),
y = "HDAC9",
xlab = "Systolic blood pressure (mmHg) by medication use",
ylab = "HDAC9 (normalized expression)",
color = "Hypertension.drugs",
palette = c("#49A01D", "#1290D9"),
add = "jitter") +
stat_compare_means(aes(group = Hypertension.drugs), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".plaque.SBPGroup_byHypertensionDrugs.pdf"), plot = last_plot())Saving 12 x 8 in imagecompare_means(HDAC9 ~ Hypertension.selfreport, group.by = "Hypertension.drugs", data = AERNASE.clin.hdac9 %>% filter(!is.na(Hypertension.selfreport) & !is.na(Hypertension.drugs)), method = "kruskal.test")ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(Hypertension.selfreport) & !is.na(Hypertension.drugs)),
x = c("Hypertension.selfreport"),
y = "HDAC9",
xlab = "Self-reported hypertension per medication use",
ylab = "HDAC9 (normalized expression)",
color = "Hypertension.drugs",
palette = c("#49A01D", "#1290D9"),
add = "jitter") +
stat_compare_means(aes(group = Hypertension.drugs), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".plaque.Hypertension.selfreport_byHypertensionDrugs.pdf"), plot = last_plot())Saving 12 x 8 in imageHypercholesterolemia & LDL levels
We want to create figures of target(s) levels stratified by hypercholesterolemia/LDL-levels, and use of lipid-lowering drugs.
- Box and Whisker plot for target(s) plaque levels by
hypercholesterolemia (
risk614) group (no, yes) - Box and Whisker plot for target(s) plaque levels by lipid-lowering drugs group (no, yes)
- Box and Whisker plot for target(s) plaque levels by LDL-levels (mmol/L) group (<100, 100-129, 130-159, 160-189, 190+)
library(dplyr)
AERNASE.clin.hdac9 <- AERNASE.clin.hdac9 %>% mutate(LDLGroup = factor(case_when(LDL_finalCU < 100 ~ "<100",
LDL_finalCU >= 100 & LDL_finalCU <= 129 ~ "100-129",
LDL_finalCU >= 130 & LDL_finalCU <= 159 ~ "130-159",
LDL_finalCU >= 160 & LDL_finalCU <= 189 ~ "160-189",
LDL_finalCU >= 190 ~ "190+")))
table(AERNASE.clin.hdac9$LDLGroup, AERNASE.clin.hdac9$Gender)
female male
<100 45 142
100-129 25 73
130-159 18 42
160-189 9 21
190+ 2 9require(sjlabelled)
AERNASE.clin.hdac9$risk614 <- to_factor(AERNASE.clin.hdac9$risk614)
# Fix plaquephenotypes
attach(AERNASE.clin.hdac9)
AERNASE.clin.hdac9[,"Hypercholesterolemia"] <- NA
AERNASE.clin.hdac9$Hypercholesterolemia[risk614 == "missing value"] <- NA
AERNASE.clin.hdac9$Hypercholesterolemia[risk614 == -999] <- NA
AERNASE.clin.hdac9$Hypercholesterolemia[risk614 == 0] <- "no"
AERNASE.clin.hdac9$Hypercholesterolemia[risk614 == 1] <- "yes"
detach(AERNASE.clin.hdac9)
table(AERNASE.clin.hdac9$risk614, AERNASE.clin.hdac9$Hypercholesterolemia)< table of extent 3 x 0 ># AEDB.temp <- subset(AEDB, select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", "risk614", "Hypercholesterolemia"))
# require(labelled)
# AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
# AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
# AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)
#
# DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)
#
# rm(AEDB.temp)Now we can draw some graphs of plaque target(s) levels per sex and hypercholesterolemia/LDL-levels group, as well as stratified by lipid-lowering drugs users as median ± interquartile range.
compare_means(HDAC9 ~ LDLGroup, data = AERNASE.clin.hdac9 %>% filter(!is.na(LDLGroup)), method = "kruskal.test")ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(LDLGroup)),
x = c("LDLGroup"),
y = "HDAC9",
xlab = "LDL (mg/dL) per gender",
ylab = "HDAC9 (normalized expression))",
color = "LDLGroup",
palette = "npg",
add = "jitter") +
stat_compare_means(label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".plaque.LDLGroups.pdf"), plot = last_plot())Saving 12 x 8 in imagecompare_means(HDAC9 ~ LDLGroup, group.by = "Gender", data = AERNASE.clin.hdac9 %>% filter(!is.na(LDLGroup)), method = "kruskal.test")ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(LDLGroup)),
x = c("LDLGroup"),
y = "HDAC9",
xlab = "LDL (mg/dL) per gender",
ylab = "HDAC9 (normalized expression))",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "jitter") +
stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".plaque.LDLGroups_byGender.pdf"), plot = last_plot())Saving 12 x 8 in imagecompare_means(HDAC9 ~ Hypercholesterolemia, data = AERNASE.clin.hdac9 %>% filter(!is.na(Hypercholesterolemia)), method = "kruskal.test")Error in kruskal.test.default(x = mf[[1L]], g = mf[[2L]]) :
all observations are in the same groupggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(Hypercholesterolemia)),
x = c("Hypercholesterolemia"),
y = "HDAC9",
xlab = "Diagnosed hypercholesterolemia",
ylab = "HDAC9 (normalized expression))",
color = "Hypercholesterolemia",
palette = "npg",
add = "jitter") +
stat_compare_means(label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".plaque.Hypercholesterolemia.pdf"), plot = last_plot())Saving 12 x 8 in imagecompare_means(HDAC9 ~ Hypercholesterolemia, group.by = "Gender", data = AERNASE.clin.hdac9 %>% filter(!is.na(Hypercholesterolemia)), method = "kruskal.test")Error in names(grouped.d$data) <- .names :
'names' attribute [1] must be the same length as the vector [0]ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(Hypercholesterolemia)),
x = c("Hypercholesterolemia"),
y = "HDAC9",
xlab = "Diagnosed hypercholesterolemia per gender",
ylab = "HDAC9 (normalized expression))",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "jitter") +
stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".plaque.Hypercholesterolemia_byGender.pdf"), plot = last_plot())Saving 12 x 8 in imagecompare_means(HDAC9 ~ Med.Statin.LLD, data = AERNASE.clin.hdac9 %>% filter(!is.na(Med.Statin.LLD)), method = "kruskal.test")ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(Med.Statin.LLD)),
x = c("Med.Statin.LLD"),
y = "HDAC9",
xlab = "Lipid-lowering drug use",
ylab = "HDAC9 (normalized expression))",
color = "Med.Statin.LLD",
palette = "npg",
add = "jitter") +
stat_compare_means(label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".plaque.Med.Statin.LLD.pdf"), plot = last_plot())Saving 12 x 8 in imagecompare_means(HDAC9 ~ Med.Statin.LLD, group.by = "Gender", data = AERNASE.clin.hdac9 %>% filter(!is.na(Med.Statin.LLD)), method = "kruskal.test")ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(Med.Statin.LLD)),
x = c("Med.Statin.LLD"),
y = "HDAC9",
xlab = "Lipid-lowering drug use per gender",
ylab = "HDAC9 (normalized expression))",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "jitter") +
stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".plaque.Med.Statin.LLD_byGender.pdf"), plot = last_plot())Saving 12 x 8 in imagecompare_means(HDAC9 ~ LDLGroup, group.by = "Med.Statin.LLD", data = AERNASE.clin.hdac9 %>% filter(!is.na(LDLGroup) & !is.na(Med.Statin.LLD)), method = "kruskal.test")ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(LDLGroup) & !is.na(Med.Statin.LLD)),
x = c("LDLGroup"),
y = "HDAC9",
xlab = "LDL (mg/dL) per LLD use",
ylab = "HDAC9 (normalized expression))",
color = "Med.Statin.LLD",
palette = c("#49A01D", "#1290D9"),
add = "jitter") +
stat_compare_means(aes(group = Med.Statin.LLD), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".plaque.LDLGroups_byMed.Statin.LLD.pdf"), plot = last_plot())Saving 12 x 8 in imagecompare_means(HDAC9 ~ Hypercholesterolemia, group.by = "Med.Statin.LLD", data = AERNASE.clin.hdac9 %>% filter(!is.na(Hypercholesterolemia) & !is.na(Med.Statin.LLD)), method = "kruskal.test")Error in names(grouped.d$data) <- .names :
'names' attribute [1] must be the same length as the vector [0]ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(Hypercholesterolemia) & !is.na(Med.Statin.LLD)),
x = c("Hypercholesterolemia"),
y = "HDAC9",
xlab = "Diagnosed hypercholesterolemia per LLD use",
ylab = "HDAC9 (normalized expression))",
color = "Med.Statin.LLD",
palette = c("#49A01D", "#1290D9"),
add = "jitter") +
stat_compare_means(aes(group = Med.Statin.LLD), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".plaque.LDLGroups_byMed.Statin.LLD.pdf"), plot = last_plot())Saving 12 x 8 in imageKidney function (eGFR)
We want to create figures of target(s) levels stratified by kidney function.
- Box and Whisker plot for target(s) plaque levels by chronic kidney disease (CKD) group (1, 2, 3, 4, 5)
- Box and Whisker plot for target(s) plaque levels by eGFR (MDRD-based) group (90+, 60-89, 30-59, <30)
library(dplyr)
AERNASE.clin.hdac9 <- AERNASE.clin.hdac9 %>% mutate(eGFRGroup = factor(case_when(GFR_MDRD < 15 ~ "<15",
GFR_MDRD >= 15 & GFR_MDRD <= 29 ~ "15-29",
GFR_MDRD >= 30 & GFR_MDRD <= 59 ~ "30-59",
GFR_MDRD >= 60 & GFR_MDRD <= 89 ~ "60-89",
GFR_MDRD >= 90 ~ "90+")))
table(AERNASE.clin.hdac9$eGFRGroup, AERNASE.clin.hdac9$Gender)
female male
15-29 2 6
30-59 38 101
60-89 84 249
90+ 25 86table(AERNASE.clin.hdac9$eGFRGroup, AERNASE.clin.hdac9$KDOQI)
No data available/missing Normal kidney function CKD 2 (Mild) CKD 3 (Moderate) CKD 4 (Severe) CKD 5 (Failure)
15-29 0 0 0 0 8 0
30-59 0 0 0 139 0 0
60-89 0 0 333 0 0 0
90+ 0 111 0 0 0 0Now we can draw some graphs of plaque target(s) levels per sex and kidney function group as median ± interquartile range.
# Global test
compare_means(HDAC9 ~ eGFRGroup, data = AERNASE.clin.hdac9 %>% filter(!is.na(eGFRGroup)), method = "kruskal.test")ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(eGFRGroup)),
x = c("eGFRGroup"),
y = "HDAC9",
xlab = "eGFR (mL/min per 1.73 m2)",
ylab = "HDAC9 (normalized expression)",
color = "eGFRGroup",
palette = "npg",
add = "jitter") +
stat_compare_means(method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".plaque.EGFR.pdf"), plot = last_plot())Saving 12 x 8 in imagecompare_means(HDAC9 ~ eGFRGroup, group.by = "Gender", data = AERNASE.clin.hdac9 %>% filter(!is.na(eGFRGroup)), method = "kruskal.test")ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(eGFRGroup)),
x = c("eGFRGroup"),
y = "HDAC9",
xlab = "eGFR (mL/min per 1.73 m2) per gender",
ylab = "HDAC9 (normalized expression)",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "jitter") +
stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".plaque.EGFR_byGender.pdf"), plot = last_plot())Saving 12 x 8 in imagecompare_means(HDAC9 ~ KDOQI, data = AERNASE.clin.hdac9 %>% filter(!is.na(KDOQI)), method = "kruskal.test")p1 <- ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(KDOQI)),
x = c("KDOQI"),
y = "HDAC9",
xlab = "Kidney function (KDOQI)",
ylab = "HDAC9 (normalized expression)",
color = "KDOQI",
palette = "npg",
add = "jitter") +
stat_compare_means(aes(group = KDOQI), label = "p.format", method = "kruskal.test")
ggpar(p1 + rotate_x_text(45), legend = "right") 
rm(p1)
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".plaque.KDOQI.pdf"), plot = last_plot())Saving 12 x 8 in imagecompare_means(HDAC9 ~ KDOQI, group.by = "Gender", data = AERNASE.clin.hdac9 %>% filter(!is.na(KDOQI)), method = "kruskal.test")p1 <- ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(KDOQI)),
x = c("KDOQI"),
y = "HDAC9",
xlab = "Kidney function (KDOQI) per gender",
ylab = "HDAC9 (normalized expression)",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "jitter") +
stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggpar(p1 + rotate_x_text(45), legend = "right") 
rm(p1)
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".plaque.KDOQI_byGender.pdf"), plot = last_plot())Saving 12 x 8 in imagecompare_means(HDAC9 ~ eGFRGroup, data = AERNASE.clin.hdac9 %>% filter(!is.na(eGFRGroup) & !is.na(KDOQI)), method = "kruskal.test")p1 <- ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(eGFRGroup) & !is.na(KDOQI)),
x = c("eGFRGroup"),
y = "HDAC9",
xlab = "eGFR (mL/min per 1.73 m2) by KDOQI group",
ylab = "HDAC9 (normalized expression)",
color = "KDOQI",
palette = "npg",
add = "jitter") +
stat_compare_means(method = "kruskal.test")
ggpar(p1, legend = "right")
rm(p1)
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".plaque.EGFR_KDOQI.pdf"), plot = last_plot())Saving 12 x 8 in imageBMI
We want to create figures of target(s) levels stratified by BMI.
- Box and Whisker plot for target(s) plaque levels by BMI WHO group (underweight, normal, overweight, obese)
- Box and Whisker plot for target(s) plaque levels by BMI group (<18.5, 18.5-24.9, 25, 29.9, 30-24.9, 35+)
library(dplyr)
AERNASE.clin.hdac9 <- AERNASE.clin.hdac9 %>% mutate(BMIGroup = factor(case_when(BMI < 18.5 ~ "<18.5",
BMI >= 18.5 & BMI < 25 ~ "18.5-24",
BMI >= 25 & BMI < 30 ~ "25-29",
BMI >= 30 & BMI < 35 ~ "30-35",
BMI >= 35 ~ "35+")))
# require(labelled)
# AERNASE.clin.hdac9$BMI_US <- as_factor(AERNASE.clin.hdac9$BMI_US)
# AERNASE.clin.hdac9$BMI_WHO <- as_factor(AERNASE.clin.hdac9$BMI_WHO)
# table(AERNASE.clin.hdac9$BMI_WHO, AERNASE.clin.hdac9$BMI_US)
table(AERNASE.clin.hdac9$BMIGroup, AERNASE.clin.hdac9$Gender)
female male
<18.5 3 2
18.5-24 46 162
25-29 68 220
30-35 18 55
35+ 6 12table(AERNASE.clin.hdac9$BMIGroup, AERNASE.clin.hdac9$BMI_WHO)
No data available/missing Underweight Normal Overweight Obese
<18.5 0 5 0 0 0
18.5-24 0 0 208 0 0
25-29 0 0 0 287 0
30-35 0 0 0 0 73
35+ 0 0 0 0 18Now we can draw some graphs of plaque MCP1 levels per sex and age group as median ± interquartile range.
# Global test
compare_means(HDAC9 ~ BMIGroup, data = AERNASE.clin.hdac9 %>% filter(!is.na(BMIGroup)), method = "kruskal.test")ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(BMIGroup)),
x = c("BMIGroup"),
y = "HDAC9",
xlab = "BMI groups (kg/m2)",
ylab = "HDAC9 (normalized expression)",
# color = "Gender",
# palette = c("#D5267B", "#1290D9"),
color = "BMIGroup",
palette = "npg",
add = "jitter") +
stat_compare_means(label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".plaque.BMI.pdf"), plot = last_plot())Saving 12 x 8 in imagecompare_means(HDAC9 ~ BMIGroup, group.by = "Gender", data = AERNASE.clin.hdac9 %>% filter(!is.na(BMIGroup)), method = "kruskal.test")ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(BMIGroup)),
x = c("BMIGroup"),
y = "HDAC9",
xlab = "BMI groups (kg/m2) per gender",
ylab = "HDAC9 (normalized expression)",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "jitter") +
stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".plaque.BMI_byGender.pdf"), plot = last_plot())Saving 12 x 8 in imagecompare_means(HDAC9 ~ BMIGroup, data = AERNASE.clin.hdac9 %>% filter(!is.na(BMIGroup) & !is.na(BMI_WHO)), method = "kruskal.test")p1 <- ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(BMIGroup) & !is.na(BMI_WHO)),
x = c("BMIGroup"),
y = "HDAC9",
xlab = "BMI groups (kg/m2) per WHO categories",
ylab = "HDAC9 (normalized expression)",
color = "BMI_WHO",
palette = "npg",
add = "jitter") +
stat_compare_means(method = "kruskal.test")
ggpar(p1, legend = "right")
rm(p1)
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".plaque.BMI_byWHO.pdf"), plot = last_plot())Saving 12 x 8 in imageDiabetes
We want to create figures of target(s) levels stratified by type 2 diabetes.
- Box and Whisker plot for target(s) plaque levels by type 2 diabetes group (no, yes)
Now we can draw some graphs of plaque target(s) levels per sex and age group as median ± interquartile range.
# Global test
compare_means(HDAC9 ~ DiabetesStatus, data = AERNASE.clin.hdac9 %>% filter(!is.na(DiabetesStatus)), method = "kruskal.test")ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(DiabetesStatus)),
x = c("DiabetesStatus"),
y = "HDAC9",
xlab = "Diabetes status",
ylab = "HDAC9 (normalized expression)",
# color = "Gender",
# palette = c("#D5267B", "#1290D9"),
color = "DiabetesStatus",
palette = "npg",
add = c("median_iqr", "jitter")) +
stat_compare_means(label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".plaque.Diabetes.pdf"), plot = last_plot())Saving 12 x 8 in imagecompare_means(HDAC9 ~ DiabetesStatus, group.by = "Gender", data = AERNASE.clin.hdac9 %>% filter(!is.na(DiabetesStatus)), method = "kruskal.test")ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(DiabetesStatus)),
x = c("DiabetesStatus"),
y = "HDAC9",
xlab = "Diabetes status per gender",
ylab = "HDAC9 (normalized expression)",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = c("median_iqr", "jitter")) +
stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".plaque.Diabetes_byGender.pdf"), plot = last_plot())Saving 12 x 8 in imageSmoking
We want to create figures of target(s) levels stratified by smoking.
- Box and Whisker plot for target(s) plaque levels by smoking group (never, ex, current)
Now we can draw some graphs of plaque target(s) levels per sex and age group as median ± interquartile range.
# Global test
compare_means(HDAC9 ~ SmokerStatus, data = AERNASE.clin.hdac9 %>% filter(!is.na(SmokerStatus)), method = "kruskal.test")ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(SmokerStatus)),
x = c("SmokerStatus"),
y = "HDAC9",
xlab = "Smoker status",
ylab = "HDAC9 (normalized expression)",
# color = "Gender",
# palette = c("#D5267B", "#1290D9"),
color = "SmokerStatus",
palette = "npg",
add = c("median_iqr", "jitter")) +
stat_compare_means(label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".plaque.Smoking.pdf"), plot = last_plot())Saving 12 x 8 in imagecompare_means(HDAC9 ~ SmokerStatus, group.by ="Gender", data = AERNASE.clin.hdac9 %>% filter(!is.na(SmokerStatus)), method = "kruskal.test")ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(SmokerStatus)),
x = c("SmokerStatus"),
y = "HDAC9",
xlab = "Smoker status per gender",
ylab = "HDAC9 (normalized expression)",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = c("median_iqr", "jitter")) +
stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".plaque.Smoking_byGender.pdf"), plot = last_plot())Saving 12 x 8 in imageStenosis
We want to create figures of target(s) levels stratified by stenosis grade.
- Box and Whisker plot for target(s) plaque levels by stenosis grade group (<70, 70-89, 90+)
library(dplyr)
AERNASE.clin.hdac9 <- AERNASE.clin.hdac9 %>% mutate(StenoticGroup = factor(case_when(stenose == "0-49%" ~ "<70",
stenose == "0-49%" ~ "<70",
stenose == "50-70%" ~ "<70",
stenose == "70-90%" ~ "70-89",
stenose == "50-99%" ~ "90+",
stenose == "70-99%" ~ "90+",
stenose == "100% (Occlusion)" ~ "90+",
stenose == "90-99%" ~ "90+")))
table(AERNASE.clin.hdac9$StenoticGroup, AERNASE.clin.hdac9$Gender)
female male
<70 6 34
70-89 72 199
90+ 69 221table(AERNASE.clin.hdac9$stenose, AERNASE.clin.hdac9$StenoticGroup)
<70 70-89 90+
missing 0 0 0
0-49% 2 0 0
50-70% 38 0 0
70-90% 0 271 0
90-99% 0 0 284
100% (Occlusion) 0 0 5
NA 0 0 0
50-99% 0 0 1
70-99% 0 0 0
99 0 0 0Now we can draw some graphs of plaque target(s) levels per sex and age group as median ± interquartile range.
# Global test
compare_means(HDAC9 ~ StenoticGroup, data = AERNASE.clin.hdac9 %>% filter(!is.na(StenoticGroup)), method = "kruskal.test")ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(StenoticGroup)),
x = c("StenoticGroup"),
y = "HDAC9",
xlab = "Stenotic grade",
ylab = "HDAC9 (normalized expression)",
# color = "Gender",
# palette = c("#D5267B", "#1290D9"),
color = "StenoticGroup",
palette = "npg",
add = "jitter") +
stat_compare_means(label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".plaque.Stenosis.pdf"), plot = last_plot())Saving 12 x 8 in imagecompare_means(HDAC9 ~ StenoticGroup, group.by = "Gender", data = AERNASE.clin.hdac9 %>% filter(!is.na(StenoticGroup)), method = "kruskal.test")ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(StenoticGroup)),
x = c("StenoticGroup"),
y = "HDAC9",
xlab = "Stenotic grade per gender",
ylab = "HDAC9 (normalized expression)",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "jitter") +
stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".plaque.Stenosis_byGender.pdf"), plot = last_plot())Saving 12 x 8 in imageSymptoms
We want to create per-symptom figures.
library(dplyr)
table(AERNASE.clin.hdac9$AgeGroup, AERNASE.clin.hdac9$AsymptSympt2G)
Asymptomatic Symptomatic
<55 10 28
55-64 24 143
65-74 30 219
75-84 15 141
85+ 1 12table(AERNASE.clin.hdac9$Gender, AERNASE.clin.hdac9$AsymptSympt2G)
Asymptomatic Symptomatic
female 15 138
male 65 405table(AERNASE.clin.hdac9$AsymptSympt2G)
Asymptomatic Symptomatic
80 543 Now we can draw some graphs of plaque target(s) levels per symptom group as median ± interquartile range.
# ?ggpubr::ggboxplot()
my_comparisons <- list(c("Asymptomatic", "Symptomatic"))
compare_means(HDAC9 ~ AsymptSympt2G, data = AERNASE.clin.hdac9, method = "kruskal.test")p1 <- ggpubr::ggboxplot(AERNASE.clin.hdac9,
x = "AsymptSympt2G", y = "HDAC9",
title = "HDAC9 (normalized expression) levels per symptom",
xlab = "Symptoms",
ylab = "HDAC9 (normalized expression)",
color = "AsymptSympt2G",
# palette = c(uithof_color[16], uithof_color[23]),
palette = "npg",
add = "dotplot", # Add dotplot
add.params = list(binwidth = 0.1, dotsize = 0.3)
) +
stat_compare_means(comparisons = my_comparisons, method = "wilcox.test")
ggpar(p1, legend = c("right"), legend.title = "Symptoms")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".plaque.AsymptSympt2G.pdf"), plot = last_plot())Saving 12 x 8 in imagerm(p1)compare_means(HDAC9 ~ AsymptSympt2G, group.by = "Gender", data = AERNASE.clin.hdac9, method = "kruskal.test")p1 <- ggpubr::ggboxplot(AERNASE.clin.hdac9,
x = "AsymptSympt2G", y = "HDAC9",
title = "HDAC9 (normalized expression) levels per symptom by gender",
xlab = "Symptoms",
ylab = "HDAC9 (normalized expression)",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "dotplot", # Add dotplot
add.params = list(binwidth = 0.1, dotsize = 0.3)
) +
stat_compare_means(aes(group = Gender), label = "p.format", method = "wilcox.test")
ggpar(p1, legend = c("right"), legend.title = "Symptoms")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".plaque.AsymptSympt2G.byGender.pdf"), plot = last_plot())Saving 12 x 8 in imagerm(p1)Alternative graph
# ?ggpubr::ggboxplot()
# compare_means(LRCH1 ~ eGFRGroup, data = AERNASE.clin %>% filter(!is.na(eGFRGroup)), method = "kruskal.test")
# ggpubr::ggboxplot(AERNASE.clin %>% filter(!is.na(eGFRGroup))
cat("Get summary statistics for target:\n")Get summary statistics for target:summary(AERNASE.clin.hdac9$HDAC9) Min. 1st Qu. Median Mean 3rd Qu. Max.
0.00 11.00 21.00 28.32 37.00 449.00 cat("\nCount number of values > 100 for target:\n")
Count number of values > 100 for target:sum(AERNASE.clin.hdac9$HDAC9 > 100)[1] 12cat("\nSetting values > 100 for target to 100.\n")
Setting values > 100 for target to 100.temp <- AERNASE.clin.hdac9 %>%
filter(!is.na(AsymptSympt2G))
temp$HDAC9[temp$HDAC9 > 100] <- 100
cat("Get summary statistics for target after fixing values:\n")Get summary statistics for target after fixing values:summary(temp$HDAC9) Min. 1st Qu. Median Mean 3rd Qu. Max.
0.00 11.00 21.00 26.71 37.00 100.00 my_comparisons <- list(c("Asymptomatic", "Symptomatic"))
compare_means(HDAC9 ~ AsymptSympt2G, data = temp, method = "kruskal.test")
p1 <- ggpubr::ggboxplot(temp,
x = "AsymptSympt2G", y = "HDAC9",
title = "HDAC9 (normalized expression) levels per symptom",
xlab = "Symptoms",
ylab = "HDAC9 (normalized expression)",
color = "AsymptSympt2G",
# palette = c(uithof_color[16], uithof_color[23]),
palette = "npg",
add = "jitter", # Add dotplot
add.params = list(binwidth = 0.1, dotsize = 0.3)
) +
stat_compare_means(comparisons = my_comparisons, method = "wilcox.test")
ggpar(p1, legend = c("right"), legend.title = "Symptoms")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".HDAC9.plaque.AsymptSympt2G.noNA_limitCount100.pdf"), plot = last_plot())Saving 12 x 8 in imageggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".HDAC9.plaque.AsymptSympt2G.noNA_limitCount100.ps"), plot = last_plot())Saving 12 x 8 in imageggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".HDAC9.plaque.AsymptSympt2G.noNA_limitCount100.png"), plot = last_plot())Saving 12 x 8 in imagerm(p1, temp)Forest plots
We would also like to visualize the multivariable analyses results.
library(ggplot2)
library(openxlsx)
model1_target <- read.xlsx(paste0(OUT_loc, "/", Today, ".AERNASE.clin.hdac9.Bin.Uni.",TRAIT_OF_INTEREST,".RANK.Symptoms.MODEL1.xlsx"))
model2_target <- read.xlsx(paste0(OUT_loc, "/", Today, ".AERNASE.clin.hdac9.Bin.Multi.",TRAIT_OF_INTEREST,".RANK.Symptoms.MODEL2.xlsx"))
model1_target$model <- "univariate"
model2_target$model <- "multivariate"
models_target <- rbind(model1_target, model2_target)
models_targetForest plots.
dat <- data.frame(group = factor(c("Age, sex-adjusted", "Age, sex, and adjusted for risk factors"),
levels=c("Age, sex, and adjusted for risk factors", "Age, sex-adjusted")),
cen = c(models_target$OR[models_target$Predictor=="HDAC9"]),
low = c(models_target$low95CI[models_target$Predictor=="HDAC9"]),
high = c(models_target$up95CI[models_target$Predictor=="HDAC9"]))
fp <- ggplot(data = dat, aes(x = group, y = cen, ymin = low, ymax = high)) +
geom_pointrange(linetype = 2, size = 1, colour = c("#1290D9", "#49A01D")) +
geom_hline(yintercept = 1, lty = 2) + # add a dotted line at x=1 after flip
coord_flip(ylim = c(0.8, 1.7)) + # flip coordinates (puts labels on y axis)
xlab("Model") + ylab("OR (95% CI) for symptomatic plaques") +
ggtitle("Normalized HDAC9 expression") +
theme_minimal() # use a white background
print(fp)
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".plaque.forest.pdf"), plot = fp)
rm(fp)HDAC9 vs. cytokines plaque levels correlations
We will plot the correlations of other cytokine plaque levels to the MCP1 plaque levels. These include:
- IL2
- IL4
- IL5
- IL6
- IL8
- IL9
- IL10
- IL12
- IL13
- IL21
- INFG
- TNFA
- MIF
- MCP1
- MIP1a
- RANTES
- MIG
- IP10
- Eotaxin1
- TARC
- PARC
- MDC
- OPG
- sICAM1
- VEGFA
- TGFB
In addition we will look at three metalloproteinases which were measured using an activity assay.
- MMP2
- MMP8
- MMP9
The proteins were measured using FACS and LUMINEX. Given the different platforms used (FACS vs. LUMINEX), we will inverse rank-normalize these variables as well to scale them to the same scale as the target(s)` plaque levels.
We will set the measurements that yielded ‘0’ to NA, as it is unlikely that any protein ever has exactly 0 copies. The ‘0’ yielded during the experiment are due to the limits of the detection.
Prepare data
# fix names
names(AEDB.CEA)[names(AEDB.CEA) == "VEFGA"] <- "VEGFA"
# fix names
names(AERNASE.clin.hdac9)[names(AERNASE.clin.hdac9) == "IL6"] <- "IL6rna"
cytokines <- c("IL2", "IL4", "IL5", "IL6", "IL8", "IL9", "IL10", "IL12", "IL13", "IL21",
"INFG", "TNFA", "MIF", "MCP1", "MIP1a", "RANTES", "MIG", "IP10", "Eotaxin1",
"TARC", "PARC", "MDC", "OPG", "sICAM1", "VEGFA", "TGFB")
metalloproteinases <- c("MMP2", "MMP8", "MMP9")
AERNASE.clin.hdac9 <- merge(AERNASE.clin.hdac9,
subset(AEDB.CEA, select = c("STUDY_NUMBER",
cytokines,
metalloproteinases)),
by.x = "STUDY_NUMBER", by.y = "STUDY_NUMBER", sort = TRUE, all.x = TRUE)
proteins_of_interest <- c(cytokines, metalloproteinases)
proteins_of_interest_rank = unlist(lapply(proteins_of_interest, paste0, "_rank"))
# make variables numerics()
AERNASE.clin.hdac9 <- AERNASE.clin.hdac9 %>%
mutate_each(funs(as.numeric), proteins_of_interest)
for(PROTEIN in 1:length(proteins_of_interest)){
# UCORBIOGSAqc$Z <- NULL
var.temp.rank = proteins_of_interest_rank[PROTEIN]
var.temp = proteins_of_interest[PROTEIN]
cat(paste0("\nSelecting ", var.temp, " and standardising: ", var.temp.rank,".\n"))
cat(paste0("* changing ", var.temp, " to numeric.\n"))
# AERNASE.clin.hdac9 <- AERNASE.clin.hdac9 %>% mutate(AERNASE.clin.hdac9[,var.temp] == replace(AERNASE.clin.hdac9[,var.temp], AERNASE.clin.hdac9[,var.temp]==0, NA))
AERNASE.clin.hdac9[,var.temp][AERNASE.clin.hdac9[,var.temp]==0.000000]=NA
cat(paste0("* standardising ", var.temp,
" (mean: ",round(mean(!is.na(AERNASE.clin.hdac9[,var.temp])), digits = 6),
", n = ",sum(!is.na(AERNASE.clin.hdac9[,var.temp])),").\n"))
AERNASE.clin.hdac9 <- AERNASE.clin.hdac9 %>%
mutate_at(vars(var.temp),
# list(Z = ~ (AERNASE.clin.hdac9[,var.temp] - mean(AERNASE.clin.hdac9[,var.temp], na.rm = TRUE))/sd(AERNASE.clin.hdac9[,var.temp], na.rm = TRUE))
list(RANK = ~ qnorm((rank(AERNASE.clin.hdac9[,var.temp], na.last = "keep") - 0.5) / sum(!is.na(AERNASE.clin.hdac9[,var.temp]))))
)
# str(UCORBIOGSAqc$Z)
cat(paste0("* renaming RANK to ", var.temp.rank,".\n"))
AERNASE.clin.hdac9[,var.temp.rank] <- NULL
names(AERNASE.clin.hdac9)[names(AERNASE.clin.hdac9) == "RANK"] <- var.temp.rank
}
# rm(var.temp, var.temp.rank)Visualize transformations
We will just visualize these transformations.
proteins_of_interest_rank_target <- c("HDAC9", proteins_of_interest_rank)
proteins_of_interest_target <- c("HDAC9", proteins_of_interest)
for(PROTEIN in proteins_of_interest_target){
cat(paste0("Plotting protein ", PROTEIN, ".\n"))
p1 <- ggpubr::gghistogram(AERNASE.clin.hdac9, PROTEIN,
# y = "..count..",
color = "white",
fill = "Gender",
palette = c("#1290D9", "#DB003F"),
add = "mean",
# rug = TRUE,
# add.params = list(color = "black", linetype = 2),
title = paste0(PROTEIN, " plaque levels"),
xlab = "",
ggtheme = theme_minimal())
print(p1)
}
for(PROTEIN in proteins_of_interest_rank_target){
cat(paste0("Plotting protein ", PROTEIN, ".\n"))
p1 <- ggpubr::gghistogram(AERNASE.clin.hdac9, PROTEIN,
# y = "..count..",
color = "white",
fill = "Gender",
palette = c("#1290D9", "#DB003F"),
add = "mean",
# rug = TRUE,
# add.params = list(color = "black", linetype = 2),
title = paste0(PROTEIN, " plaque levels"),
xlab = "inverse-normal transformation",
ggtheme = theme_minimal())
print(p1)
}
Correlations
Here we calculate correlations between target(s) and 28 other cytokines. We use Spearman’s test, thus, correlations a given in rho. Please note the indications of measurement methods:
- L: LUMINEX
- E: ELISA
- a: activity assay
# Installation of ggcorrplot()
# --------------------------------
if(!require(devtools))
install.packages("devtools")
devtools::install_github("kassambara/ggcorrplot")
library(ggcorrplot)
# Creating matrix - inverse-rank transformation
# --------------------------------
temp <- subset(AERNASE.clin.hdac9,
select = c(proteins_of_interest_rank_target)
)
# str(AEDB.CEA.temp)
matrix.RANK <- as.matrix(temp)
rm(temp)
corr_biomarkers.rank <- round(cor(matrix.RANK,
use = "pairwise.complete.obs", #the correlation or covariance between each pair of variables is computed using all complete pairs of observations on those variables
method = "spearman"), 3)
# corr_biomarkers.rank
rename_proteins_of_interest_target <- c("HDAC9 (RNA)",
"IL2", "IL4", "IL5", "IL6", "IL8", "IL9", "IL10", "IL12",
"IL13 (L)", "IL21 (L)",
"INFG", "TNFA", "MIF (L)",
"MCP1 (L)", "MIP1a (L)", "RANTES (L)", "MIG (L)", "IP10 (L)",
"Eotaxin1 (L)", "TARC (L)", "PARC (L)", "MDC (L)",
"OPG (L)", "sICAM1 (L)", "VEGFA (E)", "TGFB (E)", "MMP2 (a)", "MMP8 (a)", "MMP9 (a)")
colnames(corr_biomarkers.rank) <- c(rename_proteins_of_interest_target)
rownames(corr_biomarkers.rank) <- c(rename_proteins_of_interest_target)
corr_biomarkers_p.rank <- ggcorrplot::cor_pmat(matrix.RANK, use = "pairwise.complete.obs", method = "spearman")
# ++++++++++++++++++++++++++++
# flattenCorrMatrix
# ++++++++++++++++++++++++++++
# cormat : matrix of the correlation coefficients
# pmat : matrix of the correlation p-values
flattenCorrMatrix <- function(cormat, pmat) {
ut <- upper.tri(cormat)
data.frame(
row = rownames(cormat)[row(cormat)[ut]],
column = rownames(cormat)[col(cormat)[ut]],
cor =(cormat)[ut],
p = pmat[ut]
)
}
corr_biomarkers.rank.df <- flattenCorrMatrix(corr_biomarkers.rank, corr_biomarkers_p.rank)
names(corr_biomarkers.rank.df)[names(corr_biomarkers.rank.df) == "row"] <- "Cytokine_X"
names(corr_biomarkers.rank.df)[names(corr_biomarkers.rank.df) == "column"] <- "CytokineY"
names(corr_biomarkers.rank.df)[names(corr_biomarkers.rank.df) == "cor"] <- "SpearmanRho"
DT::datatable(corr_biomarkers.rank.df)
fwrite(corr_biomarkers.rank.df, file = paste0(OUT_loc, "/",Today,".correlation_cytokines.txt"))# Add correlation coefficients
# --------------------------------
# argument lab = TRUE
p1 <- ggcorrplot(corr_biomarkers.rank,
method = "square",
type = "lower",
title = "Cross biomarker correlations",
show.legend = TRUE, legend.title = bquote("Spearman's"~italic(rho)),
ggtheme = ggplot2::theme_minimal, outline.color = "#FFFFFF",
show.diag = TRUE,
hc.order = FALSE,
lab = FALSE,
digits = 3,
tl.cex = 16,
# xlab = c("MCP1"),
# p.mat = corr_biomarkers_p.rank, sig.level = 0.05,
colors = c("#1290D9", "#FFFFFF", "#E55738"))
p1
ggsave(filename = paste0(PLOT_loc, "/", Today, ".correlation_cytokines.png"), plot = last_plot())
ggsave(filename = paste0(PLOT_loc, "/", Today, ".correlation_cytokines.pdf"), plot = last_plot())
rm(p1)While visually attractive we are not necessarily interested in the correlations between all the cytokines, rather of target(s)` with other cytokines only.
temp <- subset(corr_biomarkers.rank.df, Cytokine_X == "HDAC9 (RNA)" )
temp$p_log10 <- -log10(temp$p)
p_threshold <- -log10(0.05/nrow(temp))
p_threshold
p1 <- ggbarplot(temp, x = "CytokineY", y = "SpearmanRho",
fill = "CytokineY", # change fill color by cyl
# color = "white", # Set bar border colors to white
palette = uithof_color, # jco journal color palett. see ?ggpar
xlab = "Cytokine",
ylab = expression("Spearman's"~italic(rho)),
sort.val = "desc", # Sort the value in dscending order
sort.by.groups = FALSE, # Don't sort inside each group
x.text.angle = 45, # Rotate vertically x axis texts
cex = 1.25
)
ggpar(p1, legend = "bottom",
legend.title = "") +
theme(axis.text.x = element_text(size = 14),
axis.text.y = element_text(size = 14),
axis.title.x = element_text(size = 18),
axis.title.y = element_text(size = 18))
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,"_vs_Cytokines.png"), plot = last_plot())
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,"_vs_Cytokines.pdf"), plot = last_plot())
rm(p1)Another version - probably not good.
temp <- subset(corr_biomarkers.rank.df, Cytokine_X == "HDAC9 (RNA)" )
temp$p_log10 <- -log10(temp$p)
p_threshold <- -log10(0.05/nrow(temp))
p_threshold
p1 <- ggdotchart(temp, x = "CytokineY", y = "p_log10",
color = "CytokineY", #fill = "CytokineY", # Color by groups
palette = uithof_color, # Custom color palette
xlab = "Cytokine",
ylab = expression(log[10]~"("~italic(p)~")-value"),
# ylim = c(0, 9),
sorting = "descending", # Sort value in descending order
add = "segments", # Add segments from y = 0 to dots
rotate = FALSE, # Rotate vertically
# group = "CytokineY", # Order by groups
dot.size = 16, # Large dot size
label = round(temp$SpearmanRho, digits = 3), # Add mpg values as dot labels
font.label = list(color = "white", size = 12,
vjust = 0.5)
)
ggpar(p1, legend = "",
legend.title = "") +
theme(axis.text.x = element_text(size = 14),
axis.text.y = element_text(size = 14),
axis.title.x = element_text(size = 18),
axis.title.y = element_text(size = 18))
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,"_vs_Cytokines.dotchart.png"), plot = last_plot())
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,"_vs_Cytokines.dotchart.pdf"), plot = last_plot())
rm(temp, p1)HDAC9 vs. cytokines plaque levels
Model 1
In this model we correct for Age, Gender, and year of surgery.
Here we use the inverse-rank normalized data - visually this is more normally distributed.
Analysis of plaque cytokines traits as a function of plaque target(s) levels.
GLM.results <- data.frame(matrix(NA, ncol = 15, nrow = 0))
cat("Running linear regression...\n")
for (protein in 1:length(TRAITS.TARGET.RANK)) {
PROTEIN = TRAITS.TARGET.RANK[protein]
cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
for (trait in 1:length(proteins_of_interest_rank)) {
TRAIT = proteins_of_interest_rank[trait]
cat(paste0("\n- processing ",TRAIT,"\n\n"))
currentDF <- as.data.frame(AERNASE.clin.hdac9 %>%
dplyr::select(., PROTEIN, TRAIT, COVARIATES_M1) %>%
filter(complete.cases(.))) %>%
filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
# for debug
# print(DT::datatable(currentDF))
# print(nrow(currentDF))
# print(str(currentDF))
### univariate
# fit <- lm(currentDF[,PROTEIN] ~ currentDF[,TRAIT] + Age + Gender + ORdate_year, data = currentDF)
fit <- lm(currentDF[,PROTEIN] ~ currentDF[,TRAIT] + Age + Gender + ORdate_epoch, data = currentDF)
model_step <- stepAIC(fit, direction = "both", trace = FALSE)
print(model_step)
print(summary(fit))
GLM.results.TEMP <- data.frame(matrix(NA, ncol = 15, nrow = 0))
GLM.results.TEMP[1,] = GLM.CON(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
GLM.results = rbind(GLM.results, GLM.results.TEMP)
}
}
cat("Edit the column names...\n")
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
"Beta", "s.e.m.",
"OR", "low95CI", "up95CI",
"T-value", "P-value", "r^2", "r^2_adj", "N", "Model_N", "Perc_Miss")
cat("Correct the variable types...\n")
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`T-value` <- as.numeric(GLM.results$`T-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2` <- as.numeric(GLM.results$`r^2`)
GLM.results$`r^2_adj` <- as.numeric(GLM.results$`r^2_adj`)
GLM.results$`N` <- as.numeric(GLM.results$`N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)DT::datatable(GLM.results)
# Save the data
cat("Writing results to Excel-file...\n")
### Univariate
library(openxlsx)
write.xlsx(GLM.results,
file = paste0(OUT_loc, "/",Today,".AERNASE.clin.hdac9.Con.Uni.",TRAIT_OF_INTEREST,"_Plaque.Cytokines_Plaques.RANK.MODEL1.xlsx"),
rowNmes = FALSE, colNames = TRUE, sheetName = "Con.Uni.PlaquePheno")
# Removing intermediates
cat("Removing intermediate files...\n")
rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)Model 2
In this model we correct for Age, Gender, year of surgery, Hypertension status, Diabetes status, current smoker status, lipid-lowering drugs (LLDs), antiplatelet medication, eGFR (MDRD), BMI, MedHx_CVD (combination of CAD history, stroke history, and peripheral interventions), and stenosis.
Here we use the inverse-rank normalized data - visually this is more normally distributed.
Analysis of plaque cytokines as a function of plaque target(s) levels.
GLM.results <- data.frame(matrix(NA, ncol = 15, nrow = 0))
cat("Running linear regression...\n")
for (protein in 1:length(TRAITS.TARGET.RANK)) {
PROTEIN = TRAITS.TARGET.RANK[protein]
cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
for (trait in 1:length(proteins_of_interest_rank)) {
TRAIT = proteins_of_interest_rank[trait]
cat(paste0("\n- processing ",TRAIT,"\n\n"))
currentDF <- as.data.frame(AERNASE.clin.hdac9 %>%
dplyr::select(., PROTEIN, TRAIT, COVARIATES_M2) %>%
filter(complete.cases(.))) %>%
filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
# for debug
# print(DT::datatable(currentDF))
# print(nrow(currentDF))
# print(str(currentDF))
### univariate
# fit <- lm(currentDF[,PROTEIN] ~ currentDF[,TRAIT] + Age + Gender + ORdate_year +
# Hypertension.composite + DiabetesStatus + SmokerStatus +
# Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
# MedHx_CVD + stenose,
# data = currentDF)
fit <- lm(currentDF[,PROTEIN] ~ currentDF[,TRAIT] + Age + Gender + ORdate_epoch +
Hypertension.composite + DiabetesStatus + SmokerStatus +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
MedHx_CVD + stenose,
data = currentDF)
model_step <- stepAIC(fit, direction = "both", trace = FALSE)
print(model_step)
print(summary(fit))
GLM.results.TEMP <- data.frame(matrix(NA, ncol = 15, nrow = 0))
GLM.results.TEMP[1,] = GLM.CON(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
GLM.results = rbind(GLM.results, GLM.results.TEMP)
}
}
cat("Edit the column names...\n")
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
"Beta", "s.e.m.",
"OR", "low95CI", "up95CI",
"T-value", "P-value", "r^2", "r^2_adj", "N", "Model_N", "Perc_Miss")
cat("Correct the variable types...\n")
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`T-value` <- as.numeric(GLM.results$`T-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2` <- as.numeric(GLM.results$`r^2`)
GLM.results$`r^2_adj` <- as.numeric(GLM.results$`r^2_adj`)
GLM.results$`N` <- as.numeric(GLM.results$`N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)DT::datatable(GLM.results)
# Save the data
cat("Writing results to Excel-file...\n")
### Univariate
library(openxlsx)
write.xlsx(GLM.results,
file = paste0(OUT_loc, "/",Today,".AERNASE.clin.hdac9.Con.Multi.",TRAIT_OF_INTEREST,"_Plaque.Cytokines_Plaques.RANK.MODEL2.xlsx"),
rowNames = FALSE, colNames = TRUE, sheetName = "Con.Multi.PlaquePheno")
# Removing intermediates
cat("Removing intermediate files...\n")
rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)HDAC9 levels vs. vulnerability index
Here we plot the levels of inverse-rank normal transformed target(s)
plaque levels from experiment 1 and 2 to the
Plaque vulnerability index.
library(sjlabelled)
AERNASE.clin.hdac9$yeartemp <- as.numeric(year(AERNASE.clin.hdac9$dateok))
attach(AERNASE.clin.hdac9)
AERNASE.clin.hdac9[,"ORyearGroup"] <- NA
AERNASE.clin.hdac9$ORyearGroup[yeartemp <= 2007] <- "< 2007"
AERNASE.clin.hdac9$ORyearGroup[yeartemp > 2007] <- "> 2007"
detach(AERNASE.clin.hdac9)
table(AERNASE.clin.hdac9$ORyearGroup, AERNASE.clin.hdac9$ORdate_year)Visualisations
# Global test
compare_means(HDAC9 ~ Plaque_Vulnerability_Index, data = AERNASE.clin.hdac9, method = "kruskal.test")
p1 <- ggpubr::ggboxplot(AERNASE.clin.hdac9,
x = "Plaque_Vulnerability_Index",
y = "HDAC9",
xlab = "Plaque vulnerability index",
ylab = "HDAC9 (normalized expression)\n",
color = "Plaque_Vulnerability_Index",
palette = "npg",
add = "jitter",
add.params = list(size = 2, jitter = 0.2)) +
stat_compare_means(label = "p.format", method = "kruskal.test") +
font("xlab", size = 17) +
font("ylab", size = 17) +
font("xy.text", size = 16) +
font("legend.title", face = "bold")
ggpar(p1, legend = "bottom", legend.title = "Plaque vulnerability index")
ggsave(filename = paste0(PLOT_loc, "/", Today, ".",TRAIT_OF_INTEREST,".plaque.PlaqueVulnerabilityIndex.pdf"), plot = last_plot())# Global test
compare_means(HDAC9 ~ Plaque_Vulnerability_Index, data = AERNASE.clin.hdac9, method = "kruskal.test")
p1 <- ggpubr::ggbarplot(AERNASE.clin.hdac9,
x = "Plaque_Vulnerability_Index",
y = "HDAC9",
xlab = "Plaque vulnerability index",
ylab = "HDAC9 (normalized expression)\n",
col = "Plaque_Vulnerability_Index",
fill = "Plaque_Vulnerability_Index",
palette = "npg",
add = "median_iqr", error.plot = "upper_errorbar") +
stat_compare_means(label = "p.format", method = "kruskal.test",
label.x = 1, label.y = 50) +
font("xlab", size = 17) +
font("ylab", size = 17) +
font("xy.text", size = 16) +
font("legend.title", face = "bold")
ggpar(p1, legend = "bottom", legend.title = "Plaque vulnerability index", ylim = c(0, 55))
ggsave(filename = paste0(PLOT_loc, "/", Today, ".",TRAIT_OF_INTEREST,".plaque.PlaqueVulnerabilityIndex.BarPlot.median_iqr.pdf"), plot = last_plot())
compare_means(HDAC9 ~ Plaque_Vulnerability_Index, data = AERNASE.clin.hdac9, method = "kruskal.test")p1 <- ggpubr::ggbarplot(AERNASE.clin.hdac9,
x = "Plaque_Vulnerability_Index",
y = "HDAC9",
xlab = "Plaque vulnerability index",
ylab = "HDAC9 (normalized expression)\n",
col = "Plaque_Vulnerability_Index",
fill = "Plaque_Vulnerability_Index",
palette = "npg",
add = "mean_se", error.plot = "upper_errorbar") +
stat_compare_means(label = "p.format", method = "kruskal.test",
label.x = 1, label.y = 50) +
font("xlab", size = 17) +
font("ylab", size = 17) +
font("xy.text", size = 16) +
font("legend.title", face = "bold")
ggpar(p1, legend = "bottom", legend.title = "Plaque vulnerability index", ylim = c(0, 55))
ggsave(filename = paste0(PLOT_loc, "/", Today, ".",TRAIT_OF_INTEREST,".plaque.PlaqueVulnerabilityIndex.BarPlot.means_se.pdf"), plot = last_plot())compare_means(HDAC9 ~ Plaque_Vulnerability_Index, data = subset(AERNASE.clin.hdac9, HDAC9 <100), method = "kruskal.test")
p1 <- ggpubr::ggboxplot(subset(AERNASE.clin.hdac9, HDAC9 <100) ,
x = "Plaque_Vulnerability_Index",
y = "HDAC9",
xlab = "Plaque vulnerability index",
ylab = "HDAC9 (normalized expression)\noutliers above 100 are removed",
col = "Plaque_Vulnerability_Index",
fill = "Plaque_Vulnerability_Index",
palette = "npg",
add = "boxplot", error.plot = "crossbar") +
stat_compare_means(label = "p.format", method = "kruskal.test",
label.x = 1, label.y = 50) +
font("xlab", size = 17) +
font("ylab", size = 17) +
font("xy.text", size = 16) +
font("legend.title", face = "bold")
ggpar(p1, legend = "bottom", legend.title = "Plaque vulnerability index")
ggsave(filename = paste0(PLOT_loc, "/", Today, ".",TRAIT_OF_INTEREST,".plaque.PlaqueVulnerabilityIndex.Boxplot.outlier_above_100_removed.pdf"), plot = last_plot())compare_means(HDAC9 ~ Plaque_Vulnerability_Index, data = AERNASE.clin.hdac9, method = "kruskal.test")
p1 <- ggpubr::ggboxplot(AERNASE.clin.hdac9,
x = "Plaque_Vulnerability_Index",
y = "HDAC9",
facet.by = "Plaque_Vulnerability_Index",
xlab = "Plaque vulnerability index",
ylab = "HDAC9 (normalized expression)\n",
color = "Plaque_Vulnerability_Index",
palette = "npg",
add = "jitter",
add.params = list(size = 2, jitter = 0.2)) +
stat_compare_means(label = "p.format", method = "kruskal.test") +
font("xlab", size = 17) +
font("ylab", size = 17) +
font("xy.text", size = 16) +
font("legend.title", face = "bold")
ggpar(p1, legend = "bottom", legend.title = "Plaque vulnerability index")
ggsave(filename = paste0(PLOT_loc, "/", Today, ".",TRAIT_OF_INTEREST,".plaque.FacetByPlaqueVulnerabilityIndex.pdf"), plot = last_plot())compare_means(HDAC9 ~ Plaque_Vulnerability_Index, group.by = "Gender", data = AERNASE.clin.hdac9, method = "kruskal.test")
p2 <- ggpubr::ggboxplot(AERNASE.clin.hdac9,
x = "Plaque_Vulnerability_Index",
y = "HDAC9",
xlab = "Plaque vulnerability index by gender",
ylab = "HDAC9 (normalized expression)\n",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "jitter") +
stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggpar(p2, legend = "bottom", legend.title = "Plaque vulnerability index")
ggsave(filename = paste0(PLOT_loc, "/", Today, ".",TRAIT_OF_INTEREST,".plaque.PlaqueVulnerabilityIndex.byGender.pdf"), plot = last_plot())
compare_means(HDAC9 ~ Plaque_Vulnerability_Index, data = AERNASE.clin.hdac9, method = "kruskal.test")
p5 <- ggpubr::ggboxplot(AERNASE.clin.hdac9,
x = "Plaque_Vulnerability_Index",
y = "HDAC9",
xlab = "Plaque vulnerability index",
ylab = "HDAC9 (normalized expression)\n",
color = "Plaque_Vulnerability_Index",
palette = "npg",
facet.by = "ORyearGroup",
add = "jitter") +
stat_compare_means(label = "p.format", method = "kruskal.test")
ggpar(p5, legend = "bottom", legend.title = "Plaque vulnerability index")
ggsave(filename = paste0(PLOT_loc, "/", Today, ".",TRAIT_OF_INTEREST,".plaque.PlaqueVulnerabilityIndex_Facet_byYear.pdf"), plot = last_plot())compare_means(HDAC9 ~ Plaque_Vulnerability_Index, group.by = "Gender", data = AERNASE.clin.hdac9, method = "kruskal.test")
p6 <- ggpubr::ggboxplot(AERNASE.clin.hdac9,
x = "Plaque_Vulnerability_Index",
y = "HDAC9",
xlab = "Plaque vulnerability index",
ylab = "HDAC9 (normalized expression)\n",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
facet.by = "ORyearGroup",
add = "jitter") +
stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggpar(p6, legend = "bottom", legend.title = "Plaque vulnerability index")
ggsave(filename = paste0(PLOT_loc, "/", Today, ".",TRAIT_OF_INTEREST,".plaque.PlaqueVulnerabilityIndex_Facet_byYear.byGender.pdf"), plot = last_plot())Model 1
In this model we correct for Age, Gender, and year of surgery.
Here we use the inverse-rank normalized data - visually this is more normally distributed.
Analysis of the plaque vulnerability indez as a function of plaque target(s) levels.
TRAITS.TARGET.RANK.extra = c("HDAC9")
GLM.results <- data.frame(matrix(NA, ncol = 16, nrow = 0))
for (protein in 1:length(TRAITS.TARGET.RANK.extra)) {
PROTEIN = TRAITS.TARGET.RANK.extra[protein]
cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
TRAIT = "Plaque_Vulnerability_Index"
cat(paste0("\n- processing ",TRAIT,"\n\n"))
currentDF <- as.data.frame(AERNASE.clin.hdac9 %>%
dplyr::select(., PROTEIN, TRAIT, COVARIATES_M1) %>%
filter(complete.cases(.))) %>%
filter_if(~is.numeric(.), all_vars(!is.infinite(.))) %>%
droplevels(.)
# fix numeric OR year
# currentDF$ORdate_year <- as.numeric(currentDF$ORdate_year)
# for debug
# print(DT::datatable(currentDF))
# print(nrow(currentDF))
# print(str(currentDF))
# print(class(currentDF[,TRAIT]))
# table(currentDF$ORdate_year)
### univariate
# + Hypertension.composite + DiabetesStatus + SmokerCurrent +
# Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
# CAD_history + Stroke_history + Peripheral.interv + stenose
# fit <- polr(currentDF[,TRAIT] ~ currentDF[,PROTEIN] + Age + Gender + ORdate_year,
# data = currentDF,
# Hess = TRUE)
fit <- polr(currentDF[,TRAIT] ~ currentDF[,PROTEIN] + Age + Gender + ORdate_epoch,
data = currentDF,
Hess = TRUE)
print(summary(fit))
## store table
(ctable <- coef(summary(fit)))
## calculate and store p values
p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2
## combined table
print((ctable <- cbind(ctable, "p value" = p)))
}Model 2
In this model we correct for Age, Gender, Hypertension status, Diabetes status, current smoker status, lipid-lowering drugs (LLDs), antiplatelet medication, eGFR (MDRD), BMI, MedHx_CVD (combination of CAD history, stroke history, and peripheral interventions), and stenosis..
for (protein in 1:length(TRAITS.TARGET.RANK.extra)) {
PROTEIN = TRAITS.TARGET.RANK.extra[protein]
cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
TRAIT = "Plaque_Vulnerability_Index"
cat(paste0("\n- processing ",TRAIT,"\n\n"))
currentDF <- as.data.frame(AERNASE.clin.hdac9 %>%
dplyr::select(., PROTEIN, TRAIT, COVARIATES_M2) %>%
filter(complete.cases(.))) %>%
filter_if(~is.numeric(.), all_vars(!is.infinite(.))) %>%
droplevels(.)
# fix numeric OR year
# currentDF$ORdate_year <- as.numeric(currentDF$ORdate_year)
# for debug
# print(DT::datatable(currentDF))
# print(nrow(currentDF))
# print(str(currentDF))
# print(class(currentDF[,TRAIT]))
### univariate
# fit <- polr(as.factor(currentDF[,TRAIT]) ~ currentDF[,PROTEIN] + Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus + SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + MedHx_CVD + stenose,
# data = currentDF,
# Hess = TRUE)
fit <- polr(as.factor(currentDF[,TRAIT]) ~ currentDF[,PROTEIN] + Age + Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + MedHx_CVD + stenose,
data = currentDF,
Hess = TRUE)
print(summary(fit))
## store table
(ctable <- coef(summary(fit)))
## calculate and store p values
p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2
## combined table
print((ctable <- cbind(ctable, "p value" = p)))
}Plotting HDAC9 vs Fat 10 perc. in the plaque
# Global test
compare_means(HDAC9 ~ Fat.bin_10, data = AERNASE.clin.hdac9 %>% filter(!is.na(Fat.bin_10)), method = "kruskal.test")
p1 <- ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(Fat.bin_10)),
x = "Fat.bin_10",
y = "HDAC9",
xlab = "Fat <10% vs >10%",
ylab = "HDAC9 (normalized expression)\n",
color = "Fat.bin_10",
palette = "npg",
add = "jitter") +
stat_compare_means(label = "p.format", method = "kruskal.test")
ggpar(p1, legend = "bottom", legend.title = "Fat <10% vs >10%")
ggsave(filename = paste0(PLOT_loc, "/", Today, ".",TRAIT_OF_INTEREST,".plaque.Fat.bin_10.pdf"), plot = last_plot())compare_means(HDAC9 ~ Fat.bin_10, group.by = "Gender", data = AERNASE.clin.hdac9 %>% filter(!is.na(Fat.bin_10)), method = "kruskal.test")
p2 <- ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(Fat.bin_10)),
x = "Fat.bin_10",
y = "HDAC9",
xlab = "Fat <10% vs >10% by gender",
ylab = "HDAC9 (normalized expression)\n",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "jitter") +
stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggpar(p2, legend = "bottom", legend.title = "Fat <10% vs >10% by gender")
ggsave(filename = paste0(PLOT_loc, "/", Today, ".",TRAIT_OF_INTEREST,".plaque.Fat.bin_10.byGender.pdf"), plot = last_plot())compare_means(HDAC9 ~ Fat.bin_10, data = AERNASE.clin.hdac9 %>% filter(!is.na(Fat.bin_10)), method = "kruskal.test")
p5 <- ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(Fat.bin_10)),
x = "Fat.bin_10",
y = "HDAC9",
xlab = "Fat <10% vs >10% by year of surgery",
ylab = "HDAC9 (normalized expression)\n",
color = "Fat.bin_10",
palette = "npg",
facet.by = "ORyearGroup",
add = "jitter") +
stat_compare_means(label = "p.format", method = "kruskal.test")
ggpar(p5, legend = "bottom", legend.title = "Fat <10% vs >10% by year of surgery")
ggsave(filename = paste0(PLOT_loc, "/", Today, ".",TRAIT_OF_INTEREST,".plaque.Fat.bin_10_Facet_byYear.pdf"), plot = last_plot())compare_means(HDAC9 ~ Fat.bin_10, group.by = "Gender", data = AERNASE.clin.hdac9 %>% filter(!is.na(Fat.bin_10)), method = "kruskal.test")
p6 <- ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(Fat.bin_10)),
x = "Fat.bin_10",
y = "HDAC9",
xlab = "Fat <10% vs >10% by year of surgery and gender",
ylab = "HDAC9 (normalized expression)\n",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
facet.by = "ORyearGroup",
add = "jitter") +
stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggpar(p6, legend = "bottom", legend.title = "Fat <10% vs >10% by year of surgery and gender")
ggsave(filename = paste0(PLOT_loc, "/", Today, ".",TRAIT_OF_INTEREST,".plaque.Fat.bin_10_Facet_byYear.byGender.pdf"), plot = last_plot())Plotting HDAC9 vs Fat 40 perc. in the plaque
# Global test
compare_means(HDAC9 ~ Fat.bin_40, data = AERNASE.clin.hdac9 %>% filter(!is.na(Fat.bin_40)), method = "kruskal.test")
p1 <- ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(Fat.bin_40)),
x = "Fat.bin_40",
y = "HDAC9",
xlab = "Fat <40% vs >40%",
ylab = "HDAC9 (normalized expression)\n",
color = "Fat.bin_40",
palette = "npg",
add = "jitter") +
stat_compare_means(label = "p.format", method = "kruskal.test")
ggpar(p1, legend = "bottom", legend.title = "Fat <40% vs >40%")
ggsave(filename = paste0(PLOT_loc, "/", Today, ".",TRAIT_OF_INTEREST,".plaque.Fat.bin_40.pdf"), plot = last_plot())compare_means(HDAC9 ~ Fat.bin_40, group.by = "Gender", data = AERNASE.clin.hdac9 %>% filter(!is.na(Fat.bin_40)), method = "kruskal.test")
p2 <- ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(Fat.bin_40)),
x = "Fat.bin_40",
y = "HDAC9",
xlab = "Fat <40% vs >40% by gender",
ylab = "HDAC9 (normalized expression)\n",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "jitter") +
stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggpar(p2, legend = "bottom", legend.title = "Fat <40% vs >40% by gender")
ggsave(filename = paste0(PLOT_loc, "/", Today, ".",TRAIT_OF_INTEREST,".plaque.Fat.bin_40.byGender.pdf"), plot = last_plot())compare_means(HDAC9 ~ Fat.bin_40, data = AERNASE.clin.hdac9 %>% filter(!is.na(Fat.bin_40)), method = "kruskal.test")
p5 <- ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(Fat.bin_40)),
x = "Fat.bin_40",
y = "HDAC9",
xlab = "Fat <40% vs >40% by year of surgery",
ylab = "HDAC9 (normalized expression)\n",
color = "Fat.bin_40",
palette = "npg",
facet.by = "ORyearGroup",
add = "jitter") +
stat_compare_means(label = "p.format", method = "kruskal.test")
ggpar(p5, legend = "bottom", legend.title = "Fat <40% vs >40% by year of surgery")
ggsave(filename = paste0(PLOT_loc, "/", Today, ".",TRAIT_OF_INTEREST,".plaque.Fat.bin_40_Facet_byYear.pdf"), plot = last_plot())compare_means(HDAC9 ~ Fat.bin_40, group.by = "Gender", data = AERNASE.clin.hdac9 %>% filter(!is.na(Fat.bin_40)), method = "kruskal.test")
p6 <- ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(Fat.bin_40)),
x = "Fat.bin_40",
y = "HDAC9",
xlab = "Fat <40% vs >40% by year of surgery and gender",
ylab = "HDAC9 (normalized expression)\n",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
facet.by = "ORyearGroup",
add = "jitter") +
stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggpar(p6, legend = "bottom", legend.title = "Fat <40% vs >40% by year of surgery and gender")
ggsave(filename = paste0(PLOT_loc, "/", Today, ".",TRAIT_OF_INTEREST,".plaque.Fat.bin_40_Facet_byYear.byGender.pdf"), plot = last_plot())Plotting HDAC9 vs IPH in the plaque
# Global test
compare_means(HDAC9 ~ IPH.bin, data = AERNASE.clin.hdac9 %>% filter(!is.na(IPH.bin)), method = "kruskal.test")
p1 <- ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(IPH.bin)),
x = "IPH.bin",
y = "HDAC9",
xlab = "Intraplaque hemorrhage (no vs. yes)",
ylab = "HDAC9 (normalized expression)\n",
color = "IPH.bin",
palette = "npg",
add = "jitter") +
stat_compare_means(label = "p.format", method = "kruskal.test")
ggpar(p1, legend = "bottom", legend.title = "IPH")
ggsave(filename = paste0(PLOT_loc, "/", Today, ".",TRAIT_OF_INTEREST,".plaque.IPH.bin.pdf"), plot = last_plot())compare_means(HDAC9 ~ IPH.bin, group.by = "Gender", data = AERNASE.clin.hdac9 %>% filter(!is.na(IPH.bin)), method = "kruskal.test")
p2 <- ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(IPH.bin)),
x = "IPH.bin",
y = "HDAC9",
xlab = "Intraplaque hemorrhage (no vs. yes) by gender",
ylab = "HDAC9 (normalized expression)\n",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "jitter") +
stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggpar(p2, legend = "bottom", legend.title = "IPH by gender")
ggsave(filename = paste0(PLOT_loc, "/", Today, ".",TRAIT_OF_INTEREST,".plaque.IPH.bin.byGender.pdf"), plot = last_plot())compare_means(HDAC9 ~ IPH.bin, data = AERNASE.clin.hdac9 %>% filter(!is.na(IPH.bin)), method = "kruskal.test")
p5 <- ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(IPH.bin)),
x = "IPH.bin",
y = "HDAC9",
xlab = "Intraplaque hemorrhage (no vs. yes) by year of surgery",
ylab = "HDAC9 (normalized expression)\n",
color = "IPH.bin",
palette = "npg",
facet.by = "ORyearGroup",
add = "jitter") +
stat_compare_means(label = "p.format", method = "kruskal.test")
ggpar(p5, legend = "bottom", legend.title = "IPH by year of surgery")
ggsave(filename = paste0(PLOT_loc, "/", Today, ".",TRAIT_OF_INTEREST,".plaque.IPH.bin_Facet_byYear.pdf"), plot = last_plot())compare_means(HDAC9 ~ IPH.bin, group.by = "Gender", data = AERNASE.clin.hdac9 %>% filter(!is.na(IPH.bin)), method = "kruskal.test")
p6 <- ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(IPH.bin)),
x = "IPH.bin",
y = "HDAC9",
xlab = "Intraplaque hemorrhage (no vs. yes) by year of surgery and gender",
ylab = "HDAC9 (normalized expression)\n",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
facet.by = "ORyearGroup",
add = "jitter") +
stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggpar(p6, legend = "bottom", legend.title = "IPH by year of surgery and gender")
ggsave(filename = paste0(PLOT_loc, "/", Today, ".",TRAIT_OF_INTEREST,".plaque.IPH.bin_Facet_byYear.byGender.pdf"), plot = last_plot())Plotting HDAC9 vs Calcification in the plaque
# Global test
compare_means(HDAC9 ~ Calc.bin, data = AERNASE.clin.hdac9 %>% filter(!is.na(Calc.bin)), method = "kruskal.test")
p1 <- ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(Calc.bin)),
x = "Calc.bin",
y = "HDAC9",
xlab = "Calcification (no/minor vs. moderate/heavy)",
ylab = "HDAC9 (normalized expression)\n",
color = "Calc.bin",
palette = "npg",
add = "jitter") +
stat_compare_means(label = "p.format", method = "kruskal.test")
ggpar(p1, legend = "bottom", legend.title = "Calcification")
ggsave(filename = paste0(PLOT_loc, "/", Today, ".",TRAIT_OF_INTEREST,".plaque.Calc.bin.pdf"), plot = last_plot())compare_means(HDAC9 ~ Calc.bin, group.by = "Gender", data = AERNASE.clin.hdac9 %>% filter(!is.na(Calc.bin)), method = "kruskal.test")
p2 <- ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(Calc.bin)),
x = "Calc.bin",
y = "HDAC9",
xlab = "Calcification (no/minor vs. moderate/heavy) by gender",
ylab = "HDAC9 (normalized expression)\n",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "jitter") +
stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggpar(p2, legend = "bottom", legend.title = "Calcification by gender")
ggsave(filename = paste0(PLOT_loc, "/", Today, ".",TRAIT_OF_INTEREST,".plaque.Calc.bin.byGender.pdf"), plot = last_plot())compare_means(HDAC9 ~ Calc.bin, data = AERNASE.clin.hdac9 %>% filter(!is.na(Calc.bin)), method = "kruskal.test")
p5 <- ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(Calc.bin)),
x = "Calc.bin",
y = "HDAC9",
xlab = "Calcification (no/minor vs. moderate/heavy) by year of surgery",
ylab = "HDAC9 (normalized expression)\n",
color = "Calc.bin",
palette = "npg",
facet.by = "ORyearGroup",
add = "jitter") +
stat_compare_means(label = "p.format", method = "kruskal.test")
ggpar(p5, legend = "bottom", legend.title = "Calcification by year of surgery")
ggsave(filename = paste0(PLOT_loc, "/", Today, ".",TRAIT_OF_INTEREST,".plaque.Calc.bin_Facet_byYear.pdf"), plot = last_plot())compare_means(HDAC9 ~ Calc.bin, group.by = "Gender", data = AERNASE.clin.hdac9 %>% filter(!is.na(Calc.bin)), method = "kruskal.test")
p6 <- ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(Calc.bin)),
x = "Calc.bin",
y = "HDAC9",
xlab = "Calcification (no/minor vs. moderate/heavy) by year of surgery and gender",
ylab = "HDAC9 (normalized expression)\n",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
facet.by = "ORyearGroup",
add = "jitter") +
stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggpar(p6, legend = "bottom", legend.title = "Calcification by year of surgery and gender")
ggsave(filename = paste0(PLOT_loc, "/", Today, ".",TRAIT_OF_INTEREST,".plaque.Calc.bin_Facet_byYear.byGender.pdf"), plot = last_plot())Session information
Version: v1.0.5
Last update: 2023-05-31
Written by: Sander W. van der Laan (s.w.vanderlaan-2[at]umcutrecht.nl).
Description: Script to analyse HDAC9 from the Ather-Express Biobank Study.
Minimum requirements: R version 3.5.2 (2018-12-20) -- 'Eggshell Igloo', macOS Mojave (10.14.2).
**MoSCoW To-Do List**
The things we Must, Should, Could, and Would have given the time we have.
_M_
_S_
_C_
_W_
**Changes log**
* v1.0.5 Fixed forest plot and alternative boxplot for symptoms.
* v1.0.4 Made histogram of PVI. Exported HDAC9 and PVI data.
* v1.0.3 Small adaptations to PVI-plots.
* v1.0.2 Changed the PVI-plot.
* v1.0.1 Added figures on fat in the plaque.
* v1.0.0 Inital version.sessionInfo()Saving environment
save.image(paste0(PROJECT_loc, "/",Today,".",PROJECTNAME,".bulkRNAseq.additional_figures.RData"))| © 1979-2023 Sander W. van der Laan | s.w.vanderlaan[at]gmail.com | vanderlaan.science. | |
---
title: "Additional Figures"
author: '[Sander W. van der Laan, PhD](https://vanderlaan.science) | s.w.vanderlaan@gmail.com.'
date: '`r Sys.Date()`'
output:
  html_notebook: 
    cache: yes
    code_folding: hide
    collapse: yes
    df_print: paged
    fig.align: center
    fig_caption: yes
    fig_height: 10
    fig_retina: 2
    fig_width: 12
    theme: paper
    toc: yes
    toc_float:
      collapsed: no
      smooth_scroll: yes
    highlight: tango
mainfont: Helvetica
subtitle: Accompanying 'Plaque expression levels of HDAC9 in association with plaque vulnerability traits and secondary vascular events in patients undergoing carotid endarterectomy, an analysis in the Athero-EXPRESS Biobank.'
editor_options:
  chunk_output_type: inline
# bibliography: references.bib
# knit: worcs::cite_all
---
# General Setup

```{r setup, include=FALSE}
# We recommend that you prepare your raw data for analysis in 'prepare_data.R',
# and end that file with either open_data(yourdata), or closed_data(yourdata).
# Then, uncomment the line below to load the original or synthetic data
# (whichever is available), to allow anyone to reproduce your code:
# load_data()

# further define some knitr-options.
knitr::opts_chunk$set(fig.width = 12, fig.height = 8, fig.path = 'Figures/', 
                      warning = TRUE, # show warnings during codebook generation
                      message = TRUE, # show messages during codebook generation
                      error = TRUE, # do not interrupt codebook generation in case of errors, 
                                    # usually better for debugging
                      echo = TRUE,  # show R code
                      eval = TRUE)

ggplot2::theme_set(ggplot2::theme_minimal())
# pander::panderOptions("table.split.table", Inf)
library("worcs")
library("rmarkdown")

```

```{r echo = FALSE}
rm(list = ls())
```

```{r LocalSystem, echo = FALSE}
### Operating System Version
### MacBook Pro
ROOT_loc = "/Users/slaan3"

### General
GENOMIC_loc = paste0(ROOT_loc, "/OneDrive - UMC Utrecht/Genomics")
AEDB_loc = paste0(GENOMIC_loc, "/Athero-Express/AE-AAA_GS_DBs")
LAB_loc = paste0(GENOMIC_loc, "/LabBusiness")

PROJECT_loc = paste0(ROOT_loc, "/git/CirculatoryHealth/AE_20211201_YAW_SWVANDERLAAN_HDAC9")

# Genetic and genomic data
STORAGE_loc = paste0(ROOT_loc, "/PLINK")
AERNA_loc = paste0(STORAGE_loc, "/_AE_ORIGINALS/AERNA")
AESCRNA_loc = paste0(STORAGE_loc, "/_AE_ORIGINALS/AESCRNA/prepped_data")
AEGSQC_loc = paste0(STORAGE_loc, "/_AE_ORIGINALS/AEGS_COMBINED_QC2018")

### SOME VARIABLES WE NEED DOWN THE LINE
TRAIT_OF_INTEREST = "HDAC9" # Phenotype
PROJECTNAME = "HDAC9"

cat("\nCreate a new analysis directory...\n")
ifelse(!dir.exists(file.path(PROJECT_loc, "/",PROJECTNAME)), 
       dir.create(file.path(PROJECT_loc, "/",PROJECTNAME)), 
       FALSE)
ANALYSIS_loc = paste0(PROJECT_loc,"/",PROJECTNAME)

ifelse(!dir.exists(file.path(ANALYSIS_loc, "/PLOTS")), 
       dir.create(file.path(ANALYSIS_loc, "/PLOTS")), 
       FALSE)
PLOT_loc = paste0(ANALYSIS_loc,"/PLOTS")

ifelse(!dir.exists(file.path(PLOT_loc, "/QC")), 
       dir.create(file.path(PLOT_loc, "/QC")), 
       FALSE)
QC_loc = paste0(PLOT_loc,"/QC")

ifelse(!dir.exists(file.path(ANALYSIS_loc, "/OUTPUT")), 
       dir.create(file.path(ANALYSIS_loc, "/OUTPUT")), 
       FALSE)
OUT_loc = paste0(ANALYSIS_loc, "/OUTPUT")

ifelse(!dir.exists(file.path(ANALYSIS_loc, "/BASELINE")), 
       dir.create(file.path(ANALYSIS_loc, "/BASELINE")), 
       FALSE)
BASELINE_loc = paste0(ANALYSIS_loc, "/BASELINE")

ifelse(!dir.exists(file.path(ANALYSIS_loc, "/COX")), 
       dir.create(file.path(ANALYSIS_loc, "/COX")), 
       FALSE)
COX_loc = paste0(ANALYSIS_loc, "/COX")



setwd(paste0(PROJECT_loc))
getwd()
list.files()

```

```{r Source functions}
source(paste0(PROJECT_loc, "/scripts/functions.R"))
```

```{r Setting: loading_packages, message=FALSE, warning=FALSE}
install.packages.auto("readr")
install.packages.auto("optparse")
install.packages.auto("tools")
install.packages.auto("dplyr")
install.packages.auto("tidyr")
install.packages.auto("naniar")

# To get 'data.table' with 'fwrite' to be able to directly write gzipped-files
# Ref: https://stackoverflow.com/questions/42788401/is-possible-to-use-fwrite-from-data-table-with-gzfile
# install.packages("data.table", repos = "https://Rdatatable.gitlab.io/data.table")
library(data.table)

install.packages.auto("tidyverse")
install.packages.auto("knitr")
install.packages.auto("DT")
install.packages.auto("MASS")
# install.packages.auto("Seurat") # latest version

# Install the devtools package from Hadley Wickham
install.packages.auto('devtools')

install.packages.auto("haven")
install.packages.auto("sjlabelled")
install.packages.auto("sjPlot")
install.packages.auto("labelled")
install.packages.auto("tableone")

install.packages.auto("ggpubr")

```

```{r Setting: Colors, include = FALSE}

Today = format(as.Date(as.POSIXlt(Sys.time())), "%Y%m%d")
Today.Report = format(as.Date(as.POSIXlt(Sys.time())), "%A, %B %d, %Y")

### UtrechtScienceParkColoursScheme
###
### WebsitetoconvertHEXtoRGB:http://hex.colorrrs.com.
### Forsomefunctionsyoushoulddividethesenumbersby255.
### 
###	No.	Color			      HEX	(RGB)						              CHR		  MAF/INFO
###---------------------------------------------------------------------------------------
###	1	  yellow			    #FBB820 (251,184,32)				      =>	1		or 1.0>INFO
###	2	  gold			      #F59D10 (245,157,16)				      =>	2		
###	3	  salmon			    #E55738 (229,87,56)				      =>	3		or 0.05<MAF<0.2 or 0.4<INFO<0.6
###	4	  darkpink		    #DB003F ((219,0,63)				      =>	4		
###	5	  lightpink		    #E35493 (227,84,147)				      =>	5		or 0.8<INFO<1.0
###	6	  pink			      #D5267B (213,38,123)				      =>	6		
###	7	  hardpink		    #CC0071 (204,0,113)				      =>	7		
###	8	  lightpurple	    #A8448A (168,68,138)				      =>	8		
###	9	  purple			    #9A3480 (154,52,128)				      =>	9		
###	10	lavendel		    #8D5B9A (141,91,154)				      =>	10		
###	11	bluepurple		  #705296 (112,82,150)				      =>	11		
###	12	purpleblue		  #686AA9 (104,106,169)			      =>	12		
###	13	lightpurpleblue	#6173AD (97,115,173/101,120,180)	=>	13		
###	14	seablue			    #4C81BF (76,129,191)				      =>	14		
###	15	skyblue			    #2F8BC9 (47,139,201)				      =>	15		
###	16	azurblue		    #1290D9 (18,144,217)				      =>	16		or 0.01<MAF<0.05 or 0.2<INFO<0.4
###	17	lightazurblue	  #1396D8 (19,150,216)				      =>	17		
###	18	greenblue		    #15A6C1 (21,166,193)				      =>	18		
###	19	seaweedgreen	  #5EB17F (94,177,127)				      =>	19		
###	20	yellowgreen		  #86B833 (134,184,51)				      =>	20		
###	21	lightmossgreen	#C5D220 (197,210,32)				      =>	21		
###	22	mossgreen		    #9FC228 (159,194,40)				      =>	22		or MAF>0.20 or 0.6<INFO<0.8
###	23	lightgreen	  	#78B113 (120,177,19)				      =>	23/X
###	24	green			      #49A01D (73,160,29)				      =>	24/Y
###	25	grey			      #595A5C (89,90,92)				        =>	25/XY	or MAF<0.01 or 0.0<INFO<0.2
###	26	lightgrey		    #A2A3A4	(162,163,164)			      =>	26/MT
###
###	ADDITIONAL COLORS
###	27	midgrey			#D7D8D7
###	28	verylightgrey	#ECECEC"
###	29	white			#FFFFFF
###	30	black			#000000
###----------------------------------------------------------------------------------------------

uithof_color = c("#FBB820","#F59D10","#E55738","#DB003F","#E35493","#D5267B",
                 "#CC0071","#A8448A","#9A3480","#8D5B9A","#705296","#686AA9",
                 "#6173AD","#4C81BF","#2F8BC9","#1290D9","#1396D8","#15A6C1",
                 "#5EB17F","#86B833","#C5D220","#9FC228","#78B113","#49A01D",
                 "#595A5C","#A2A3A4", "#D7D8D7", "#ECECEC", "#FFFFFF", "#000000")

uithof_color_legend = c("#FBB820", "#F59D10", "#E55738", "#DB003F", "#E35493",
                        "#D5267B", "#CC0071", "#A8448A", "#9A3480", "#8D5B9A",
                        "#705296", "#686AA9", "#6173AD", "#4C81BF", "#2F8BC9",
                        "#1290D9", "#1396D8", "#15A6C1", "#5EB17F", "#86B833",
                        "#C5D220", "#9FC228", "#78B113", "#49A01D", "#595A5C",
                        "#A2A3A4", "#D7D8D7", "#ECECEC", "#FFFFFF", "#000000")


### ----------------------------------------------------------------------------
```


# Background

This notebook contains additional figures of the project "Plaque expression levels of _HDAC9_ in association with plaque vulnerability traits and secondary vascular events in patients undergoing carotid endarterectomy: an analysis in the Athero-EXPRESS Biobank.".


# Loading data

```{r Loading project data}
# load(paste0(PROJECT_loc, "/",Today,".",PROJECTNAME,".bulkRNAseq.main_analysis.RData"))
load(paste0(PROJECT_loc, "/20220319.",PROJECTNAME,".bulkRNAseq.main_analysis.RData"))

```


# Fix some variables

We need to get the 'conventional unit' versions of cholesterols.

```{r}
AERNASE.clin.hdac9 <- merge(AERNASE.clin.hdac9, 
                            subset(AEDB.CEA, select = c("STUDY_NUMBER", 
                                                        "risk614", 
                                                        "LDL_finalCU", "HDL_finalCU", "TC_finalCU", "TG_finalCU")), 
                            by.x = "STUDY_NUMBER", by.y = "STUDY_NUMBER", sort = TRUE, all.x = TRUE)
```


# Additional figures

## Age and sex
We want to create per-age-group figures median ± interquartile range. 

- Box and Whisker plot for target(s) plaque levels by sex.
- Box and Whisker plot for target(s) plaque levels by (sex and) age group (<55, 55-64, 65-74, 75-84, 85+).


```{r per Sex}

# ?ggpubr::ggboxplot()
compare_means(HDAC9 ~ Gender,  data = AERNASE.clin.hdac9, method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin.hdac9, 
                  x = c("Gender"),
                  y = "HDAC9", 
                  xlab = "gender",
                  ylab = "HDAC9 (normalized expression)",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") +
  stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".plaque.Gender.pdf"), plot = last_plot())


```

```{r AgeGroups}
library(dplyr)

AERNASE.clin.hdac9 <- AERNASE.clin.hdac9 %>% dplyr::mutate(AgeGroup = factor(case_when(Age < 55 ~ "<55",
                                                     Age >= 55  & Age <= 64 ~ "55-64",
                                                     Age >= 65  & Age <= 74 ~ "65-74",
                                                     Age >= 75  & Age <= 84 ~ "75-84",
                                                     Age >= 85 ~ "85+"))) 

AERNASE.clin.hdac9 <- AERNASE.clin.hdac9 %>% dplyr::mutate(AgeGroupSex = factor(case_when(Age < 55 & Gender == "male" ~ "<55 males" ,
                                                        Age >= 55  & Age <= 64 & Gender == "male"~ "55-64 males",
                                                        Age >= 65  & Age <= 74 & Gender == "male"~ "65-74 males",
                                                        Age >= 75  & Age <= 84 & Gender == "male"~ "75-84 males",
                                                        Age >= 85 & Gender == "male"~ "85+ males",
                                                        Age < 55 & Gender == "female" ~ "<55 females" ,
                                                        Age >= 55  & Age <= 64 & Gender == "female"~ "55-64 females ",
                                                        Age >= 65  & Age <= 74 & Gender == "female"~ "65-74 females",
                                                        Age >= 75  & Age <= 84 & Gender == "female"~ "75-84 females",
                                                        Age >= 85 & Gender == "female"~ "85+ females")))

table(AERNASE.clin.hdac9$AgeGroup, AERNASE.clin.hdac9$Gender)
table(AERNASE.clin.hdac9$AgeGroupSex)

```

Now we can draw some graphs of plaque target(s) levels per sex and age group as median ± interquartile range.

```{r per AgeGroup per Sex}

# ?ggpubr::ggboxplot()
compare_means(HDAC9 ~ AgeGroup,  data = AERNASE.clin.hdac9, method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin.hdac9, 
                  x = c("AgeGroup"),
                  y = "HDAC9", 
                  xlab = "Age groups (years)",
                  ylab = "HDAC9 (normalized expression)",
                  color = "AgeGroup",
                  palette = "npg",
                  # add = "median_iqr")
                  add = c("median_iqr", "jitter")) +
  stat_compare_means(aes(group = AgeGroup), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".plaque.AgeGroup.pdf"), plot = last_plot())

compare_means(HDAC9 ~ AgeGroup, group.by = "Gender", data = AERNASE.clin.hdac9, method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin.hdac9, 
                  x = c("AgeGroup"),
                  y = "HDAC9", 
                  xlab = "Age groups (years) per gender",
                  ylab = "HDAC9 (normalized expression",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  # add = "median_iqr")
                  add = c("median_iqr", "jitter")) +
  stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".plaque.AgeGroup_perGender.pdf"), plot = last_plot())
```


## Hypertension & blood pressure
We want to create figures of target(s) levels stratified by hypertension/blood pressure, and use of anti-hypertensive drugs. 

- Box and Whisker plot for target(s) plaque levels by hypertension group (no, yes)
- Box and Whisker plot for target(s) plaque levels by systolic blood pressure group (<120, 120-139, 140-159,160+)


```{r BloodPressure}
library(dplyr)

AERNASE.clin.hdac9 <- AERNASE.clin.hdac9 %>% mutate(SBPGroup = factor(case_when(systolic < 120 ~ "<120",
                                                     systolic >= 120  & systolic <= 139 ~ "120-139",
                                                     systolic >= 140  & systolic <= 159 ~ "140-159",
                                                     systolic >= 160 ~ "160+"))) 

table(AERNASE.clin.hdac9$SBPGroup, AERNASE.clin.hdac9$Gender)

```

Now we can draw some graphs of plaque target(s) levels per sex and hypertension/blood pressure group as median ± interquartile range.

```{r per BloodPressure per Sex}
compare_means(HDAC9 ~ SBPGroup, data = AERNASE.clin.hdac9 %>% filter(!is.na(SBPGroup)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(SBPGroup)), 
                  x = c("SBPGroup"),
                  y = "HDAC9", 
                  xlab = "Systolic blood pressure (mmHg)",
                  ylab = "HDAC9 (normalized expression)",
                  color = "SBPGroup",
                  palette = "npg",
                  add = "jitter") +
  stat_compare_means(aes(group = SBPGroup), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".plaque.SBPGroup.pdf"), plot = last_plot())

compare_means(HDAC9 ~ Hypertension.selfreport, data = AERNASE.clin.hdac9 %>% filter(!is.na(Hypertension.selfreport)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(Hypertension.selfreport)), 
                  x = c("Hypertension.selfreport"),
                  y = "HDAC9", 
                  xlab = "Self-reported hypertension",
                  ylab = "HDAC9 (normalized expression)",
                  color = "Hypertension.selfreport",
                  palette = "npg",
                  add = "jitter") +
  stat_compare_means(aes(group = Hypertension.selfreport), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".plaque.Hypertension.pdf"), plot = last_plot())

compare_means(HDAC9 ~ Hypertension.drugs, data = AERNASE.clin.hdac9 %>% filter(!is.na(Hypertension.drugs)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(Hypertension.drugs)), 
                  x = c("Hypertension.drugs"),
                  y = "HDAC9", 
                  xlab = "Hypertension medication use",
                  ylab = "HDAC9 (normalized expression)",
                  color = "Hypertension.drugs",
                  palette = "npg",
                  add = "jitter") +
  stat_compare_means(aes(group = Hypertension.drugs), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".plaque.HypertensionDrugs.pdf"), plot = last_plot())



compare_means(HDAC9 ~ SBPGroup, group.by = "Gender", data = AERNASE.clin.hdac9 %>% filter(!is.na(SBPGroup)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(SBPGroup)), 
                  x = c("SBPGroup"),
                  y = "HDAC9", 
                  xlab = "Systolic blood pressure (mmHg) per gender",
                  ylab = "HDAC9 (normalized expression)",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") +
  stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".plaque.SBPGroup_byGender.pdf"), plot = last_plot())

compare_means(HDAC9 ~ Hypertension.selfreport, group.by = "Gender", data = AERNASE.clin.hdac9 %>% filter(!is.na(Hypertension.selfreport)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(Hypertension.selfreport)), 
                  x = c("Hypertension.selfreport"),
                  y = "HDAC9", 
                  xlab = "Self-reported hypertension per gender",
                  ylab = "HDAC9 (normalized expression)",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") +
  stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".plaque.Hypertension_byGender.pdf"), plot = last_plot())

compare_means(HDAC9 ~ Hypertension.drugs, group.by = "Gender", data = AERNASE.clin.hdac9 %>% filter(!is.na(Hypertension.drugs)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(Hypertension.drugs)), 
                  x = c("Hypertension.drugs"),
                  y = "HDAC9", 
                  xlab = "Hypertension medication use per gender",
                  ylab = "HDAC9 (normalized expression)",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") +
  stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".plaque.Hypertension.drugs_byGender.pdf"), plot = last_plot())



compare_means(HDAC9 ~ SBPGroup, group.by = "Hypertension.drugs", data = AERNASE.clin.hdac9 %>% filter(!is.na(SBPGroup) & !is.na(Hypertension.drugs)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(SBPGroup) & !is.na(Hypertension.drugs)), 
                  x = c("SBPGroup"),
                  y = "HDAC9", 
                  xlab = "Systolic blood pressure (mmHg) by medication use",
                  ylab = "HDAC9 (normalized expression)",
                  color = "Hypertension.drugs",
                  palette = c("#49A01D", "#1290D9"),
                  add = "jitter") +
  stat_compare_means(aes(group = Hypertension.drugs), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".plaque.SBPGroup_byHypertensionDrugs.pdf"), plot = last_plot())

compare_means(HDAC9 ~ Hypertension.selfreport, group.by = "Hypertension.drugs", data = AERNASE.clin.hdac9 %>% filter(!is.na(Hypertension.selfreport) & !is.na(Hypertension.drugs)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(Hypertension.selfreport) & !is.na(Hypertension.drugs)), 
                  x = c("Hypertension.selfreport"),
                  y = "HDAC9", 
                  xlab = "Self-reported hypertension per medication use",
                  ylab = "HDAC9 (normalized expression)",
                  color = "Hypertension.drugs",
                  palette = c("#49A01D", "#1290D9"),
                  add = "jitter") +
  stat_compare_means(aes(group = Hypertension.drugs), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".plaque.Hypertension.selfreport_byHypertensionDrugs.pdf"), plot = last_plot())

```


## Hypercholesterolemia & LDL levels
We want to create figures of target(s) levels stratified by hypercholesterolemia/LDL-levels, and use of lipid-lowering drugs. 

- Box and Whisker plot for target(s) plaque levels by hypercholesterolemia (`risk614`) group (no, yes)
- Box and Whisker plot for target(s) plaque levels by lipid-lowering drugs group (no, yes)
- Box and Whisker plot for target(s) plaque levels by LDL-levels (mmol/L) group (<100, 100-129, 130-159, 160-189, 190+)

```{r LDLGroups}
library(dplyr)

AERNASE.clin.hdac9 <- AERNASE.clin.hdac9 %>% mutate(LDLGroup = factor(case_when(LDL_finalCU < 100 ~ "<100",
                                                     LDL_finalCU >= 100  & LDL_finalCU <= 129 ~ "100-129",
                                                     LDL_finalCU >= 130  & LDL_finalCU <= 159 ~ "130-159",
                                                     LDL_finalCU >= 160  & LDL_finalCU <= 189 ~ "160-189",
                                                     LDL_finalCU >= 190 ~ "190+"))) 


table(AERNASE.clin.hdac9$LDLGroup, AERNASE.clin.hdac9$Gender)

```


```{r Fix Hypercholesterolemia, message=FALSE, warning=FALSE}
require(sjlabelled)

AERNASE.clin.hdac9$risk614 <- to_factor(AERNASE.clin.hdac9$risk614)

# Fix plaquephenotypes
attach(AERNASE.clin.hdac9)
AERNASE.clin.hdac9[,"Hypercholesterolemia"] <- NA
AERNASE.clin.hdac9$Hypercholesterolemia[risk614 == "missing value"] <- NA
AERNASE.clin.hdac9$Hypercholesterolemia[risk614 == -999] <- NA
AERNASE.clin.hdac9$Hypercholesterolemia[risk614 == 0] <- "no"
AERNASE.clin.hdac9$Hypercholesterolemia[risk614 == 1] <- "yes"
detach(AERNASE.clin.hdac9)

table(AERNASE.clin.hdac9$risk614, AERNASE.clin.hdac9$Hypercholesterolemia)

# AEDB.temp <- subset(AEDB,  select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", "risk614", "Hypercholesterolemia"))
# require(labelled)
# AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
# AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
# AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)
# 
# DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)
# 
# rm(AEDB.temp)

```

Now we can draw some graphs of plaque target(s) levels per sex and hypercholesterolemia/LDL-levels group, as well as stratified by lipid-lowering drugs users as median ± interquartile range.

```{r per Hypercholesterolemia per Sex}

compare_means(HDAC9 ~ LDLGroup, data = AERNASE.clin.hdac9 %>% filter(!is.na(LDLGroup)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(LDLGroup)), 
                  x = c("LDLGroup"),
                  y = "HDAC9", 
                  xlab = "LDL (mg/dL) per gender",
                  ylab = "HDAC9 (normalized expression))",
                  color = "LDLGroup",
                  palette = "npg",
                  add = "jitter") +
  stat_compare_means(label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".plaque.LDLGroups.pdf"), plot = last_plot())

compare_means(HDAC9 ~ LDLGroup, group.by = "Gender", data = AERNASE.clin.hdac9 %>% filter(!is.na(LDLGroup)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(LDLGroup)), 
                  x = c("LDLGroup"),
                  y = "HDAC9", 
                  xlab = "LDL (mg/dL) per gender",
                  ylab = "HDAC9 (normalized expression))",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") +
  stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".plaque.LDLGroups_byGender.pdf"), plot = last_plot())



compare_means(HDAC9 ~ Hypercholesterolemia, data = AERNASE.clin.hdac9 %>% filter(!is.na(Hypercholesterolemia)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(Hypercholesterolemia)), 
                  x = c("Hypercholesterolemia"),
                  y = "HDAC9", 
                  xlab = "Diagnosed hypercholesterolemia",
                  ylab = "HDAC9 (normalized expression))",
                  color = "Hypercholesterolemia",
                  palette = "npg",
                  add = "jitter") +
  stat_compare_means(label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".plaque.Hypercholesterolemia.pdf"), plot = last_plot())

compare_means(HDAC9 ~ Hypercholesterolemia, group.by = "Gender", data = AERNASE.clin.hdac9 %>% filter(!is.na(Hypercholesterolemia)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(Hypercholesterolemia)), 
                  x = c("Hypercholesterolemia"),
                  y = "HDAC9", 
                  xlab = "Diagnosed hypercholesterolemia per gender",
                  ylab = "HDAC9 (normalized expression))",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") +
  stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".plaque.Hypercholesterolemia_byGender.pdf"), plot = last_plot())


compare_means(HDAC9 ~ Med.Statin.LLD, data = AERNASE.clin.hdac9 %>% filter(!is.na(Med.Statin.LLD)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(Med.Statin.LLD)), 
                  x = c("Med.Statin.LLD"),
                  y = "HDAC9", 
                  xlab = "Lipid-lowering drug use",
                  ylab = "HDAC9 (normalized expression))",
                  color = "Med.Statin.LLD",
                  palette = "npg",
                  add = "jitter") +
  stat_compare_means(label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".plaque.Med.Statin.LLD.pdf"), plot = last_plot())

compare_means(HDAC9 ~ Med.Statin.LLD, group.by = "Gender", data = AERNASE.clin.hdac9 %>% filter(!is.na(Med.Statin.LLD)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(Med.Statin.LLD)), 
                  x = c("Med.Statin.LLD"),
                  y = "HDAC9", 
                  xlab = "Lipid-lowering drug use per gender",
                  ylab = "HDAC9 (normalized expression))",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") +
  stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".plaque.Med.Statin.LLD_byGender.pdf"), plot = last_plot())




compare_means(HDAC9 ~ LDLGroup, group.by = "Med.Statin.LLD", data = AERNASE.clin.hdac9 %>% filter(!is.na(LDLGroup) & !is.na(Med.Statin.LLD)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(LDLGroup) & !is.na(Med.Statin.LLD)), 
                  x = c("LDLGroup"),
                  y = "HDAC9", 
                  xlab = "LDL (mg/dL) per LLD use",
                  ylab = "HDAC9 (normalized expression))",
                  color = "Med.Statin.LLD",
                  palette = c("#49A01D", "#1290D9"),
                  add = "jitter") +
  stat_compare_means(aes(group = Med.Statin.LLD), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".plaque.LDLGroups_byMed.Statin.LLD.pdf"), plot = last_plot())

compare_means(HDAC9 ~ Hypercholesterolemia, group.by = "Med.Statin.LLD", data = AERNASE.clin.hdac9 %>% filter(!is.na(Hypercholesterolemia) & !is.na(Med.Statin.LLD)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(Hypercholesterolemia) & !is.na(Med.Statin.LLD)), 
                  x = c("Hypercholesterolemia"),
                  y = "HDAC9", 
                  xlab = "Diagnosed hypercholesterolemia per LLD use",
                  ylab = "HDAC9 (normalized expression))",
                  color = "Med.Statin.LLD",
                  palette = c("#49A01D", "#1290D9"),
                  add = "jitter") +
  stat_compare_means(aes(group = Med.Statin.LLD), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".plaque.LDLGroups_byMed.Statin.LLD.pdf"), plot = last_plot())


```



## Kidney function (eGFR)
We want to create figures of target(s) levels stratified by kidney function. 

- Box and Whisker plot for target(s) plaque levels by chronic kidney disease (CKD) group (1, 2, 3, 4, 5)
- Box and Whisker plot for target(s) plaque levels by eGFR (MDRD-based) group (90+, 60-89, 30-59, <30)

```{r EGFR}
library(dplyr)

AERNASE.clin.hdac9 <- AERNASE.clin.hdac9 %>% mutate(eGFRGroup = factor(case_when(GFR_MDRD < 15 ~ "<15",
                                                             GFR_MDRD >= 15  & GFR_MDRD <= 29 ~ "15-29",
                                                             GFR_MDRD >= 30  & GFR_MDRD <= 59 ~ "30-59",
                                                             GFR_MDRD >= 60  & GFR_MDRD <= 89 ~ "60-89",
                                                             GFR_MDRD >= 90 ~ "90+")))

table(AERNASE.clin.hdac9$eGFRGroup, AERNASE.clin.hdac9$Gender)

table(AERNASE.clin.hdac9$eGFRGroup, AERNASE.clin.hdac9$KDOQI)

```

Now we can draw some graphs of plaque target(s) levels per sex and kidney function group as median ± interquartile range.


```{r per EGFR per Sex}

# Global test

compare_means(HDAC9 ~ eGFRGroup, data = AERNASE.clin.hdac9 %>% filter(!is.na(eGFRGroup)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(eGFRGroup)), 
                  x = c("eGFRGroup"),
                  y = "HDAC9", 
                  xlab = "eGFR (mL/min per 1.73 m2)",
                  ylab = "HDAC9 (normalized expression)",
                  color = "eGFRGroup",
                  palette = "npg",
                  add = "jitter") +
  stat_compare_means(method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".plaque.EGFR.pdf"), plot = last_plot())

compare_means(HDAC9 ~ eGFRGroup, group.by = "Gender",  data = AERNASE.clin.hdac9 %>% filter(!is.na(eGFRGroup)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(eGFRGroup)), 
                  x = c("eGFRGroup"),
                  y = "HDAC9", 
                  xlab = "eGFR (mL/min per 1.73 m2) per gender",
                  ylab = "HDAC9 (normalized expression)",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") +
  stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".plaque.EGFR_byGender.pdf"), plot = last_plot())

compare_means(HDAC9 ~ KDOQI, data = AERNASE.clin.hdac9 %>% filter(!is.na(KDOQI)), method = "kruskal.test")
p1 <- ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(KDOQI)), 
                  x = c("KDOQI"),
                  y = "HDAC9", 
                  xlab = "Kidney function (KDOQI)",
                  ylab = "HDAC9 (normalized expression)",
                  color = "KDOQI",
                  palette = "npg",
                  add = "jitter") +
  stat_compare_means(aes(group = KDOQI), label = "p.format", method = "kruskal.test")
ggpar(p1 + rotate_x_text(45), legend = "right") 
rm(p1)
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".plaque.KDOQI.pdf"), plot = last_plot())

compare_means(HDAC9 ~ KDOQI, group.by = "Gender",   data = AERNASE.clin.hdac9 %>% filter(!is.na(KDOQI)), method = "kruskal.test")
p1 <- ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(KDOQI)), 
                  x = c("KDOQI"),
                  y = "HDAC9", 
                  xlab = "Kidney function (KDOQI) per gender",
                  ylab = "HDAC9 (normalized expression)",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") +
  stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggpar(p1 + rotate_x_text(45), legend = "right") 
rm(p1)
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".plaque.KDOQI_byGender.pdf"), plot = last_plot())

compare_means(HDAC9 ~ eGFRGroup,  data = AERNASE.clin.hdac9 %>% filter(!is.na(eGFRGroup) & !is.na(KDOQI)), method = "kruskal.test")
p1 <- ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(eGFRGroup) & !is.na(KDOQI)), 
                  x = c("eGFRGroup"),
                  y = "HDAC9", 
                  xlab = "eGFR (mL/min per 1.73 m2) by KDOQI group",
                  ylab = "HDAC9 (normalized expression)",
                  color = "KDOQI",
                  palette = "npg",
                  add = "jitter") +
  stat_compare_means(method = "kruskal.test")
ggpar(p1, legend = "right")
rm(p1)
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".plaque.EGFR_KDOQI.pdf"), plot = last_plot())

```



## BMI
We want to create figures of target(s) levels stratified by BMI. 

- Box and Whisker plot for target(s) plaque levels by BMI WHO group (underweight, normal, overweight, obese)
- Box and Whisker plot for target(s) plaque levels by BMI group (<18.5, 18.5-24.9, 25, 29.9, 30-24.9, 35+)

```{r BMI}
library(dplyr)

AERNASE.clin.hdac9 <- AERNASE.clin.hdac9 %>% mutate(BMIGroup = factor(case_when(BMI < 18.5 ~ "<18.5",
                                                     BMI >= 18.5  & BMI < 25 ~ "18.5-24",
                                                     BMI >= 25  & BMI < 30 ~ "25-29",
                                                     BMI >= 30  & BMI < 35 ~ "30-35",
                                                     BMI >= 35 ~ "35+"))) 

# require(labelled)
# AERNASE.clin.hdac9$BMI_US <- as_factor(AERNASE.clin.hdac9$BMI_US)
# AERNASE.clin.hdac9$BMI_WHO <- as_factor(AERNASE.clin.hdac9$BMI_WHO)
# table(AERNASE.clin.hdac9$BMI_WHO, AERNASE.clin.hdac9$BMI_US)

table(AERNASE.clin.hdac9$BMIGroup, AERNASE.clin.hdac9$Gender)
table(AERNASE.clin.hdac9$BMIGroup, AERNASE.clin.hdac9$BMI_WHO)

```

Now we can draw some graphs of plaque MCP1 levels per sex and age group as median ± interquartile range.


```{r per BMI per Sex}

# Global test
compare_means(HDAC9 ~ BMIGroup,  data = AERNASE.clin.hdac9 %>% filter(!is.na(BMIGroup)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(BMIGroup)), 
                  x = c("BMIGroup"),
                  y = "HDAC9", 
                  xlab = "BMI groups (kg/m2)",
                  ylab = "HDAC9 (normalized expression)",
                  # color = "Gender",
                  # palette = c("#D5267B", "#1290D9"),
                  color = "BMIGroup",
                  palette = "npg",
                  add = "jitter") +
  stat_compare_means(label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".plaque.BMI.pdf"), plot = last_plot())

compare_means(HDAC9 ~ BMIGroup, group.by = "Gender", data = AERNASE.clin.hdac9 %>% filter(!is.na(BMIGroup)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(BMIGroup)), 
                  x = c("BMIGroup"),
                  y = "HDAC9", 
                  xlab = "BMI groups (kg/m2) per gender",
                  ylab = "HDAC9 (normalized expression)",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") +
  stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".plaque.BMI_byGender.pdf"), plot = last_plot())

compare_means(HDAC9 ~ BMIGroup,  data = AERNASE.clin.hdac9 %>% filter(!is.na(BMIGroup) & !is.na(BMI_WHO)), method = "kruskal.test")
p1 <- ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(BMIGroup) & !is.na(BMI_WHO)), 
                  x = c("BMIGroup"),
                  y = "HDAC9", 
                  xlab = "BMI groups (kg/m2) per WHO categories",
                  ylab = "HDAC9 (normalized expression)",
                  color = "BMI_WHO",
                  palette = "npg",
                  add = "jitter") +
  stat_compare_means(method = "kruskal.test")
ggpar(p1, legend = "right")
rm(p1)
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".plaque.BMI_byWHO.pdf"), plot = last_plot())

```


## Diabetes
We want to create figures of target(s) levels stratified by type 2 diabetes. 

- Box and Whisker plot for target(s) plaque levels by type 2 diabetes group (no, yes)

Now we can draw some graphs of plaque target(s) levels per sex and age group as median ± interquartile range.


```{r per Diabetes per Sex}

# Global test
compare_means(HDAC9 ~ DiabetesStatus,  data = AERNASE.clin.hdac9 %>% filter(!is.na(DiabetesStatus)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(DiabetesStatus)), 
                  x = c("DiabetesStatus"),
                  y = "HDAC9", 
                  xlab = "Diabetes status",
                  ylab = "HDAC9 (normalized expression)",
                  # color = "Gender",
                  # palette = c("#D5267B", "#1290D9"),
                  color = "DiabetesStatus",
                  palette = "npg",
                  add = c("median_iqr", "jitter")) +
  stat_compare_means(label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".plaque.Diabetes.pdf"), plot = last_plot())

compare_means(HDAC9 ~ DiabetesStatus, group.by = "Gender", data = AERNASE.clin.hdac9 %>% filter(!is.na(DiabetesStatus)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(DiabetesStatus)), 
                  x = c("DiabetesStatus"),
                  y = "HDAC9", 
                  xlab = "Diabetes status per gender",
                  ylab = "HDAC9 (normalized expression)",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = c("median_iqr", "jitter")) +
  stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".plaque.Diabetes_byGender.pdf"), plot = last_plot())


```



## Smoking
We want to create figures of target(s) levels stratified by smoking. 

- Box and Whisker plot for target(s) plaque levels by smoking group (never, ex, current)

Now we can draw some graphs of plaque target(s) levels per sex and age group as median ± interquartile range.


```{r per Smoking per Sex}

# Global test
compare_means(HDAC9 ~ SmokerStatus,  data = AERNASE.clin.hdac9 %>% filter(!is.na(SmokerStatus)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(SmokerStatus)), 
                  x = c("SmokerStatus"),
                  y = "HDAC9", 
                  xlab = "Smoker status",
                  ylab = "HDAC9 (normalized expression)",
                  # color = "Gender",
                  # palette = c("#D5267B", "#1290D9"),
                  color = "SmokerStatus",
                  palette = "npg",
                  add = c("median_iqr", "jitter")) +
  stat_compare_means(label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".plaque.Smoking.pdf"), plot = last_plot())

compare_means(HDAC9 ~ SmokerStatus, group.by ="Gender", data = AERNASE.clin.hdac9 %>% filter(!is.na(SmokerStatus)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(SmokerStatus)), 
                  x = c("SmokerStatus"),
                  y = "HDAC9", 
                  xlab = "Smoker status per gender",
                  ylab = "HDAC9 (normalized expression)",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = c("median_iqr", "jitter")) +
  stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".plaque.Smoking_byGender.pdf"), plot = last_plot())

```



## Stenosis
We want to create figures of target(s) levels stratified by stenosis grade. 

- Box and Whisker plot for target(s) plaque levels by stenosis grade group (<70, 70-89, 90+)

```{r Stenosis}
library(dplyr)

AERNASE.clin.hdac9 <- AERNASE.clin.hdac9 %>% mutate(StenoticGroup = factor(case_when(stenose == "0-49%" ~ "<70",
                                                     stenose == "0-49%" ~ "<70",
                                                     stenose == "50-70%" ~ "<70",
                                                     stenose == "70-90%" ~ "70-89",
                                                     stenose == "50-99%" ~ "90+",
                                                     stenose == "70-99%" ~ "90+",
                                                     stenose == "100% (Occlusion)" ~ "90+",
                                                     stenose == "90-99%" ~ "90+")))

table(AERNASE.clin.hdac9$StenoticGroup, AERNASE.clin.hdac9$Gender)
table(AERNASE.clin.hdac9$stenose, AERNASE.clin.hdac9$StenoticGroup)

```

Now we can draw some graphs of plaque target(s) levels per sex and age group as median ± interquartile range.

```{r per Stenosis per Sex}

# Global test
compare_means(HDAC9 ~ StenoticGroup,  data = AERNASE.clin.hdac9 %>% filter(!is.na(StenoticGroup)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(StenoticGroup)), 
                  x = c("StenoticGroup"),
                  y = "HDAC9", 
                  xlab = "Stenotic grade",
                  ylab = "HDAC9 (normalized expression)",
                  # color = "Gender",
                  # palette = c("#D5267B", "#1290D9"),
                  color = "StenoticGroup",
                  palette = "npg",
                  add = "jitter") +
  stat_compare_means(label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".plaque.Stenosis.pdf"), plot = last_plot())

compare_means(HDAC9 ~ StenoticGroup, group.by = "Gender", data = AERNASE.clin.hdac9 %>% filter(!is.na(StenoticGroup)), method = "kruskal.test")
ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(StenoticGroup)), 
                  x = c("StenoticGroup"),
                  y = "HDAC9", 
                  xlab = "Stenotic grade per gender",
                  ylab = "HDAC9 (normalized expression)",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") +
  stat_compare_means(aes(group = Gender), label = "p.format", method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".plaque.Stenosis_byGender.pdf"), plot = last_plot())

```


## Symptoms
We want to create per-symptom figures. 

```{r SymptomGroups}
library(dplyr)

table(AERNASE.clin.hdac9$AgeGroup, AERNASE.clin.hdac9$AsymptSympt2G)
table(AERNASE.clin.hdac9$Gender, AERNASE.clin.hdac9$AsymptSympt2G)
table(AERNASE.clin.hdac9$AsymptSympt2G)

```

Now we can draw some graphs of plaque target(s) levels per symptom group as median ± interquartile range.

```{r per SymptomGroups}

# ?ggpubr::ggboxplot()
my_comparisons <- list(c("Asymptomatic", "Symptomatic"))

compare_means(HDAC9 ~ AsymptSympt2G, data = AERNASE.clin.hdac9, method = "kruskal.test")
p1 <- ggpubr::ggboxplot(AERNASE.clin.hdac9, 
                  x = "AsymptSympt2G", y = "HDAC9",
                  title = "HDAC9 (normalized expression) levels per symptom", 
                  xlab = "Symptoms",
                  ylab = "HDAC9 (normalized expression)",
                  color = "AsymptSympt2G", 
                  # palette = c(uithof_color[16], uithof_color[23]),
                  palette = "npg",
                  add = "dotplot", # Add dotplot
                  add.params = list(binwidth = 0.1, dotsize = 0.3)
          ) +
  stat_compare_means(comparisons = my_comparisons, method = "wilcox.test")
ggpar(p1, legend = c("right"), legend.title = "Symptoms")

ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".plaque.AsymptSympt2G.pdf"), plot = last_plot())

rm(p1)
```

```{r per SymptomGroups by Gender}
compare_means(HDAC9 ~ AsymptSympt2G, group.by = "Gender", data = AERNASE.clin.hdac9, method = "kruskal.test")
p1 <- ggpubr::ggboxplot(AERNASE.clin.hdac9, 
                  x = "AsymptSympt2G", y = "HDAC9",
                  title = "HDAC9 (normalized expression) levels per symptom by gender", 
                  xlab = "Symptoms",
                  ylab = "HDAC9 (normalized expression)",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "dotplot", # Add dotplot
                  add.params = list(binwidth = 0.1, dotsize = 0.3)
          ) +
  stat_compare_means(aes(group = Gender), label = "p.format",  method = "wilcox.test")
ggpar(p1, legend = c("right"), legend.title = "Symptoms")

ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".plaque.AsymptSympt2G.byGender.pdf"), plot = last_plot())

rm(p1)

```

### Alternative graph
```{r}
# ?ggpubr::ggboxplot()
# compare_means(LRCH1 ~ eGFRGroup, data = AERNASE.clin %>% filter(!is.na(eGFRGroup)), method = "kruskal.test")
# ggpubr::ggboxplot(AERNASE.clin %>% filter(!is.na(eGFRGroup))

cat("Get summary statistics for target:\n")
summary(AERNASE.clin.hdac9$HDAC9)

cat("\nCount number of values > 100 for target:\n")
sum(AERNASE.clin.hdac9$HDAC9 > 100)

cat("\nSetting values > 100 for target to 100.\n")
temp <- AERNASE.clin.hdac9 %>% 
  filter(!is.na(AsymptSympt2G))

temp$HDAC9[temp$HDAC9 > 100] <- 100

cat("Get summary statistics for target after fixing values:\n")
summary(temp$HDAC9)

```


```{r}
my_comparisons <- list(c("Asymptomatic", "Symptomatic"))

compare_means(HDAC9 ~ AsymptSympt2G, data = temp, method = "kruskal.test")

p1 <- ggpubr::ggboxplot(temp, 
                  x = "AsymptSympt2G", y = "HDAC9",
                  title = "HDAC9 (normalized expression) levels per symptom", 
                  xlab = "Symptoms",
                  ylab = "HDAC9 (normalized expression)",
                  color = "AsymptSympt2G", 
                  # palette = c(uithof_color[16], uithof_color[23]),
                  palette = "npg",
                  add = "jitter", # Add dotplot
                  add.params = list(binwidth = 0.1, dotsize = 0.3)
          ) +
  stat_compare_means(comparisons = my_comparisons, method = "wilcox.test")
ggpar(p1, legend = c("right"), legend.title = "Symptoms")

ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".HDAC9.plaque.AsymptSympt2G.noNA_limitCount100.pdf"), plot = last_plot())
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".HDAC9.plaque.AsymptSympt2G.noNA_limitCount100.ps"), plot = last_plot())
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.",TRAIT_OF_INTEREST,".HDAC9.plaque.AsymptSympt2G.noNA_limitCount100.png"), plot = last_plot())

rm(p1, temp)
```



## Forest plots

We would also like to visualize the multivariable analyses results.
```{r load model data}
library(ggplot2)
library(openxlsx)
model1_target <- read.xlsx(paste0(OUT_loc, "/", Today, ".AERNASE.clin.hdac9.Bin.Uni.",TRAIT_OF_INTEREST,".RANK.Symptoms.MODEL1.xlsx"))
model2_target <- read.xlsx(paste0(OUT_loc, "/", Today, ".AERNASE.clin.hdac9.Bin.Multi.",TRAIT_OF_INTEREST,".RANK.Symptoms.MODEL2.xlsx"))

model1_target$model <- "univariate"
model2_target$model <- "multivariate"

models_target <- rbind(model1_target, model2_target)
models_target

```

Forest plots.

```{r forestplot plaque, experiment 2}
dat <- data.frame(group = factor(c("Age, sex-adjusted", "Age, sex, and adjusted for risk factors"), 
                           
                           levels=c("Age, sex, and adjusted for risk factors", "Age, sex-adjusted")),
                  cen = c(models_target$OR[models_target$Predictor=="HDAC9"]),
                  low = c(models_target$low95CI[models_target$Predictor=="HDAC9"]),
                  high = c(models_target$up95CI[models_target$Predictor=="HDAC9"]))

fp <- ggplot(data = dat, aes(x = group, y = cen, ymin = low, ymax = high)) +
  geom_pointrange(linetype = 2, size = 1, colour = c("#1290D9", "#49A01D")) + 
  geom_hline(yintercept = 1, lty = 2) +  # add a dotted line at x=1 after flip
  coord_flip(ylim = c(0.8, 1.7)) +  # flip coordinates (puts labels on y axis)
  xlab("Model") + ylab("OR (95% CI) for symptomatic plaques") +
  ggtitle("Normalized HDAC9 expression") +
  theme_minimal()  # use a white background
print(fp)

ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,".plaque.forest.pdf"), plot = fp)

rm(fp)
```


## HDAC9 vs. cytokines plaque levels correlations

We will plot the correlations of other cytokine plaque levels to the MCP1 plaque levels. These include:

- IL2
- IL4
- IL5
- IL6
- IL8
- IL9
- IL10
- IL12
- IL13
- IL21
- INFG
- TNFA
- MIF
- MCP1
- MIP1a
- RANTES
- MIG
- IP10
- Eotaxin1
- TARC
- PARC
- MDC
- OPG
- sICAM1
- VEGFA
- TGFB

In addition we will look at three metalloproteinases which were measured using an activity assay. 

- MMP2
- MMP8
- MMP9

The proteins were measured using FACS and LUMINEX. Given the different platforms used (FACS vs. LUMINEX), we will inverse rank-normalize these variables as well to scale them to the same scale as the target(s)` plaque levels.


We will set the measurements that yielded '0' to NA, as it is unlikely that any protein ever has exactly 0 copies. The '0' yielded during the experiment are due to the limits of the detection.

### Prepare data

```{r}
# fix names
names(AEDB.CEA)[names(AEDB.CEA) == "VEFGA"] <- "VEGFA"

# fix names
names(AERNASE.clin.hdac9)[names(AERNASE.clin.hdac9) == "IL6"] <- "IL6rna"

cytokines <- c("IL2", "IL4", "IL5", "IL6", "IL8", "IL9", "IL10", "IL12", "IL13", "IL21", 
               "INFG", "TNFA", "MIF", "MCP1", "MIP1a", "RANTES", "MIG", "IP10", "Eotaxin1", 
               "TARC", "PARC", "MDC", "OPG", "sICAM1", "VEGFA", "TGFB")
metalloproteinases <- c("MMP2", "MMP8", "MMP9")


AERNASE.clin.hdac9 <- merge(AERNASE.clin.hdac9, 
                            subset(AEDB.CEA, select = c("STUDY_NUMBER", 
                                                        cytokines,
                                                        metalloproteinases)), 
                            by.x = "STUDY_NUMBER", by.y = "STUDY_NUMBER", sort = TRUE, all.x = TRUE)

```


```{r HDAC9 vs Cytokines INRT, paged.print=TRUE}

proteins_of_interest <- c(cytokines, metalloproteinases)

proteins_of_interest_rank = unlist(lapply(proteins_of_interest, paste0, "_rank"))

# make variables numerics()
AERNASE.clin.hdac9 <- AERNASE.clin.hdac9 %>%
  mutate_each(funs(as.numeric), proteins_of_interest)
  
for(PROTEIN in 1:length(proteins_of_interest)){

  # UCORBIOGSAqc$Z <- NULL
  var.temp.rank = proteins_of_interest_rank[PROTEIN]
  var.temp = proteins_of_interest[PROTEIN]
  
  cat(paste0("\nSelecting ", var.temp, " and standardising: ", var.temp.rank,".\n"))
  cat(paste0("* changing ", var.temp, " to numeric.\n"))

  # AERNASE.clin.hdac9 <-  AERNASE.clin.hdac9 %>% mutate(AERNASE.clin.hdac9[,var.temp] == replace(AERNASE.clin.hdac9[,var.temp], AERNASE.clin.hdac9[,var.temp]==0, NA))

  AERNASE.clin.hdac9[,var.temp][AERNASE.clin.hdac9[,var.temp]==0.000000]=NA

  cat(paste0("* standardising ", var.temp, 
             " (mean: ",round(mean(!is.na(AERNASE.clin.hdac9[,var.temp])), digits = 6),
             ", n = ",sum(!is.na(AERNASE.clin.hdac9[,var.temp])),").\n"))
  
  AERNASE.clin.hdac9 <- AERNASE.clin.hdac9 %>%
      mutate_at(vars(var.temp), 
        # list(Z = ~ (AERNASE.clin.hdac9[,var.temp] - mean(AERNASE.clin.hdac9[,var.temp], na.rm = TRUE))/sd(AERNASE.clin.hdac9[,var.temp], na.rm = TRUE))
        list(RANK = ~ qnorm((rank(AERNASE.clin.hdac9[,var.temp], na.last = "keep") - 0.5) / sum(!is.na(AERNASE.clin.hdac9[,var.temp]))))
      )
  # str(UCORBIOGSAqc$Z)
  cat(paste0("* renaming RANK to ", var.temp.rank,".\n"))
  AERNASE.clin.hdac9[,var.temp.rank] <- NULL
  names(AERNASE.clin.hdac9)[names(AERNASE.clin.hdac9) == "RANK"] <- var.temp.rank
}

# rm(var.temp, var.temp.rank)

```

### Visualize transformations

We will just visualize these transformations.

```{r HDAC9 vs Cytokines Histograms}
proteins_of_interest_rank_target <- c("HDAC9", proteins_of_interest_rank)

proteins_of_interest_target <- c("HDAC9", proteins_of_interest)

for(PROTEIN in proteins_of_interest_target){
  cat(paste0("Plotting protein ", PROTEIN, ".\n"))
  
  p1 <- ggpubr::gghistogram(AERNASE.clin.hdac9, PROTEIN,
                    # y = "..count..",
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"),
                    add = "mean",
                    # rug = TRUE,
                    # add.params =  list(color = "black", linetype = 2),
                    title = paste0(PROTEIN, " plaque levels"),
                    xlab = "",
                    ggtheme = theme_minimal())
  print(p1)
  
}


for(PROTEIN in proteins_of_interest_rank_target){
  cat(paste0("Plotting protein ", PROTEIN, ".\n"))
  
  p1 <- ggpubr::gghistogram(AERNASE.clin.hdac9, PROTEIN,
                    # y = "..count..",
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"),
                    add = "mean",
                    # rug = TRUE,
                    # add.params =  list(color = "black", linetype = 2),
                    title = paste0(PROTEIN, " plaque levels"),
                    xlab = "inverse-normal transformation",
                    ggtheme = theme_minimal())
  print(p1)
  
}
  
```

### Correlations

Here we calculate correlations between target(s) and 28 other cytokines. We use Spearman's test, thus, correlations a given in _rho_. Please note the indications of measurement methods:

- _L_: LUMINEX
- _E_: ELISA
- _a_: activity assay

```{r MCP1 vs Cytokines correlations}
# Installation of ggcorrplot()
# --------------------------------
if(!require(devtools)) 
  install.packages("devtools")
devtools::install_github("kassambara/ggcorrplot")

library(ggcorrplot)

# Creating matrix - inverse-rank transformation
# --------------------------------
temp <- subset(AERNASE.clin.hdac9, 
                          select = c(proteins_of_interest_rank_target)
                                    )

# str(AEDB.CEA.temp)
matrix.RANK <- as.matrix(temp)
rm(temp)

corr_biomarkers.rank <- round(cor(matrix.RANK, 
                             use = "pairwise.complete.obs", #the correlation or covariance between each pair of variables is computed using all complete pairs of observations on those variables
                             method = "spearman"), 3)
# corr_biomarkers.rank

rename_proteins_of_interest_target <- c("HDAC9 (RNA)", 
                                    "IL2", "IL4", "IL5", "IL6", "IL8", "IL9", "IL10", "IL12", 
                                    "IL13 (L)", "IL21 (L)", 
                                    "INFG", "TNFA", "MIF (L)", 
                                    "MCP1 (L)", "MIP1a (L)", "RANTES (L)", "MIG (L)", "IP10 (L)", 
                                    "Eotaxin1 (L)", "TARC (L)", "PARC (L)", "MDC (L)", 
                                    "OPG (L)", "sICAM1 (L)", "VEGFA (E)", "TGFB (E)", "MMP2 (a)", "MMP8 (a)", "MMP9 (a)")
colnames(corr_biomarkers.rank) <- c(rename_proteins_of_interest_target)
rownames(corr_biomarkers.rank) <- c(rename_proteins_of_interest_target)

corr_biomarkers_p.rank <- ggcorrplot::cor_pmat(matrix.RANK, use = "pairwise.complete.obs", method = "spearman")

# ++++++++++++++++++++++++++++
# flattenCorrMatrix
# ++++++++++++++++++++++++++++
# cormat : matrix of the correlation coefficients
# pmat : matrix of the correlation p-values
flattenCorrMatrix <- function(cormat, pmat) {
  ut <- upper.tri(cormat)
  data.frame(
    row = rownames(cormat)[row(cormat)[ut]],
    column = rownames(cormat)[col(cormat)[ut]],
    cor  =(cormat)[ut],
    p = pmat[ut]
    )
}

corr_biomarkers.rank.df <- flattenCorrMatrix(corr_biomarkers.rank, corr_biomarkers_p.rank)


names(corr_biomarkers.rank.df)[names(corr_biomarkers.rank.df) == "row"] <- "Cytokine_X"
names(corr_biomarkers.rank.df)[names(corr_biomarkers.rank.df) == "column"] <- "CytokineY"
names(corr_biomarkers.rank.df)[names(corr_biomarkers.rank.df) == "cor"] <- "SpearmanRho"

DT::datatable(corr_biomarkers.rank.df)

fwrite(corr_biomarkers.rank.df, file = paste0(OUT_loc, "/",Today,".correlation_cytokines.txt"))

```

```{r MCP1 vs Cytokines heatmap}
# Add correlation coefficients
# --------------------------------
# argument lab = TRUE
p1 <- ggcorrplot(corr_biomarkers.rank, 
           method = "square", 
           type = "lower",
           title = "Cross biomarker correlations", 
           show.legend = TRUE, legend.title = bquote("Spearman's"~italic(rho)),
           ggtheme = ggplot2::theme_minimal, outline.color = "#FFFFFF",
           show.diag = TRUE,
           hc.order = FALSE, 
           lab = FALSE,
           digits = 3,
           tl.cex = 16,
           # xlab = c("MCP1"),
           # p.mat = corr_biomarkers_p.rank, sig.level = 0.05,
           colors = c("#1290D9", "#FFFFFF", "#E55738"))
p1
ggsave(filename = paste0(PLOT_loc, "/", Today, ".correlation_cytokines.png"), plot = last_plot())
ggsave(filename = paste0(PLOT_loc, "/", Today, ".correlation_cytokines.pdf"), plot = last_plot())

rm(p1)

```

While visually attractive we are not necessarily interested in the correlations between all the cytokines, rather of target(s)` with other cytokines only.

```{r MCP1 vs Cytokines barplot}
temp <- subset(corr_biomarkers.rank.df, Cytokine_X == "HDAC9 (RNA)" )
temp$p_log10 <- -log10(temp$p)
p_threshold <- -log10(0.05/nrow(temp))
p_threshold
p1 <- ggbarplot(temp, x = "CytokineY", y = "SpearmanRho",
          fill = "CytokineY",               # change fill color by cyl
          # color = "white",            # Set bar border colors to white
          palette = uithof_color,            # jco journal color palett. see ?ggpar
          xlab = "Cytokine",
          ylab = expression("Spearman's"~italic(rho)),
          sort.val = "desc",          # Sort the value in dscending order
          sort.by.groups = FALSE,     # Don't sort inside each group
          x.text.angle = 45, # Rotate vertically x axis texts
          cex = 1.25
          )
ggpar(p1, legend = "bottom", 
      legend.title = "") +
  theme(axis.text.x = element_text(size = 14),
        axis.text.y = element_text(size = 14),
        axis.title.x = element_text(size = 18),
        axis.title.y = element_text(size = 18)) 

ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,"_vs_Cytokines.png"), plot = last_plot())
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,"_vs_Cytokines.pdf"), plot = last_plot())
rm(p1)


```

Another version - probably not good. 
```{r MCP1 vs Cytokines dotchart}
temp <- subset(corr_biomarkers.rank.df, Cytokine_X == "HDAC9 (RNA)" )
temp$p_log10 <- -log10(temp$p)
p_threshold <- -log10(0.05/nrow(temp))
p_threshold
p1 <- ggdotchart(temp, x = "CytokineY", y = "p_log10",
           color = "CytokineY", #fill = "CytokineY",                              # Color by groups
           palette = uithof_color, # Custom color palette
           xlab = "Cytokine",
           ylab = expression(log[10]~"("~italic(p)~")-value"),
           # ylim = c(0, 9),
           sorting = "descending",                       # Sort value in descending order
           add = "segments",                             # Add segments from y = 0 to dots
           rotate = FALSE,                                # Rotate vertically
           # group = "CytokineY",                                # Order by groups
           dot.size = 16,                                 # Large dot size
           label = round(temp$SpearmanRho, digits = 3),                        # Add mpg values as dot labels
           font.label = list(color = "white", size = 12, 
                             vjust = 0.5)                   
           )
ggpar(p1, legend = "", 
      legend.title = "") +
  theme(axis.text.x = element_text(size = 14),
        axis.text.y = element_text(size = 14),
        axis.title.x = element_text(size = 18),
        axis.title.y = element_text(size = 18))

ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,"_vs_Cytokines.dotchart.png"), plot = last_plot())
ggsave(file = paste0(PLOT_loc, "/",Today,".AERNASE.clin.hdac9.",TRAIT_OF_INTEREST,"_vs_Cytokines.dotchart.pdf"), plot = last_plot())

rm(temp, p1)

```


## HDAC9 vs. cytokines plaque levels

### Model 1

In this model we correct for _Age_, _Gender_, and _year of surgery_.

Here we use the inverse-rank normalized data - visually this is more normally distributed.

Analysis of plaque cytokines traits as a function of plaque target(s) levels.

```{r CrossSec: Cytokines - linear regression MODEL1 RANK, paged.print=TRUE}

GLM.results <- data.frame(matrix(NA, ncol = 15, nrow = 0))
cat("Running linear regression...\n")
for (protein in 1:length(TRAITS.TARGET.RANK)) {
  PROTEIN = TRAITS.TARGET.RANK[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  for (trait in 1:length(proteins_of_interest_rank)) {
    TRAIT = proteins_of_interest_rank[trait]
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AERNASE.clin.hdac9 %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M1) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    ### univariate
    # fit <- lm(currentDF[,PROTEIN] ~ currentDF[,TRAIT] + Age + Gender + ORdate_year, data = currentDF)
    fit <- lm(currentDF[,PROTEIN] ~ currentDF[,TRAIT] + Age + Gender + ORdate_epoch, data = currentDF)
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))

    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 15, nrow = 0))
    GLM.results.TEMP[1,] = GLM.CON(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
}
cat("Edit the column names...\n")
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "T-value", "P-value", "r^2", "r^2_adj", "N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`T-value` <- as.numeric(GLM.results$`T-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2` <- as.numeric(GLM.results$`r^2`)
GLM.results$`r^2_adj` <- as.numeric(GLM.results$`r^2_adj`)
GLM.results$`N` <- as.numeric(GLM.results$`N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

```

```{r CrossSec: Cytokines - linear regression MODEL1 RANK Writing}
DT::datatable(GLM.results)

# Save the data
cat("Writing results to Excel-file...\n")
### Univariate
library(openxlsx)
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AERNASE.clin.hdac9.Con.Uni.",TRAIT_OF_INTEREST,"_Plaque.Cytokines_Plaques.RANK.MODEL1.xlsx"),
           rowNmes = FALSE, colNames = TRUE, sheetName = "Con.Uni.PlaquePheno")
# Removing intermediates
cat("Removing intermediate files...\n")
rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)


```



### Model 2

In this model we correct for _Age_, _Gender_, _year of surgery_, _Hypertension status_, _Diabetes status_, _current smoker status_, _lipid-lowering drugs (LLDs)_, _antiplatelet medication_, _eGFR (MDRD)_, _BMI_, _MedHx_CVD_ (combination of _CAD history_, _stroke history_, and _peripheral interventions_), and _stenosis_.

Here we use the inverse-rank normalized data - visually this is more normally distributed.

Analysis of plaque cytokines as a function of plaque target(s) levels.

```{r CrossSec: Cytokines - linear regression MODEL2 RANK, paged.print=TRUE}

GLM.results <- data.frame(matrix(NA, ncol = 15, nrow = 0))
cat("Running linear regression...\n")
for (protein in 1:length(TRAITS.TARGET.RANK)) {
  PROTEIN = TRAITS.TARGET.RANK[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  for (trait in 1:length(proteins_of_interest_rank)) {
    TRAIT = proteins_of_interest_rank[trait]
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AERNASE.clin.hdac9 %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M2) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    ### univariate
    # fit <- lm(currentDF[,PROTEIN] ~ currentDF[,TRAIT] + Age + Gender + ORdate_year + 
    #             Hypertension.composite + DiabetesStatus + SmokerStatus + 
    #             Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
    #             MedHx_CVD + stenose, 
    #           data = currentDF)
    fit <- lm(currentDF[,PROTEIN] ~ currentDF[,TRAIT] + Age + Gender + ORdate_epoch + 
                Hypertension.composite + DiabetesStatus + SmokerStatus + 
                Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
                MedHx_CVD + stenose, 
              data = currentDF)
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))
    
    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 15, nrow = 0))
    GLM.results.TEMP[1,] = GLM.CON(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
}
cat("Edit the column names...\n")
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "T-value", "P-value", "r^2", "r^2_adj", "N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`T-value` <- as.numeric(GLM.results$`T-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2` <- as.numeric(GLM.results$`r^2`)
GLM.results$`r^2_adj` <- as.numeric(GLM.results$`r^2_adj`)
GLM.results$`N` <- as.numeric(GLM.results$`N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

```

```{r CrossSec: Cytokines - linear regression MODEL2 RANK, writing}
DT::datatable(GLM.results)

# Save the data
cat("Writing results to Excel-file...\n")
### Univariate
library(openxlsx)
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AERNASE.clin.hdac9.Con.Multi.",TRAIT_OF_INTEREST,"_Plaque.Cytokines_Plaques.RANK.MODEL2.xlsx"),
           rowNames = FALSE, colNames = TRUE, sheetName = "Con.Multi.PlaquePheno")
# Removing intermediates
cat("Removing intermediate files...\n")
rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)


```

## HDAC9 levels vs. vulnerability index


Here we plot the levels of inverse-rank normal transformed target(s) plaque levels from experiment 1 and 2 to the `Plaque vulnerability index`. 

```{r Fix ORyearGroup, message=FALSE, warning=FALSE}
library(sjlabelled)

AERNASE.clin.hdac9$yeartemp <- as.numeric(year(AERNASE.clin.hdac9$dateok))

attach(AERNASE.clin.hdac9)

AERNASE.clin.hdac9[,"ORyearGroup"] <- NA
AERNASE.clin.hdac9$ORyearGroup[yeartemp <= 2007] <- "< 2007"
AERNASE.clin.hdac9$ORyearGroup[yeartemp > 2007] <- "> 2007"
detach(AERNASE.clin.hdac9)

table(AERNASE.clin.hdac9$ORyearGroup, AERNASE.clin.hdac9$ORdate_year)
```

### Visualisations

```{r per PlaqueVulnerabilityIndex}
# Global test
compare_means(HDAC9 ~ Plaque_Vulnerability_Index,  data = AERNASE.clin.hdac9, method = "kruskal.test")
p1 <- ggpubr::ggboxplot(AERNASE.clin.hdac9, 
                  x = "Plaque_Vulnerability_Index",
                  y = "HDAC9", 
                  xlab = "Plaque vulnerability index",
                  ylab = "HDAC9 (normalized expression)\n",
                  color = "Plaque_Vulnerability_Index",
                  palette = "npg",
                  add = "jitter", 
                  add.params = list(size = 2, jitter = 0.2)) +
  stat_compare_means(label = "p.format",  method = "kruskal.test") +
  font("xlab", size = 17) +
  font("ylab", size = 17) +
  font("xy.text", size = 16) +
  font("legend.title", face = "bold") 
  
ggpar(p1, legend = "bottom", legend.title = "Plaque vulnerability index")

ggsave(filename = paste0(PLOT_loc, "/", Today, ".",TRAIT_OF_INTEREST,".plaque.PlaqueVulnerabilityIndex.pdf"), plot = last_plot())
```

```{r }
# Global test
compare_means(HDAC9 ~ Plaque_Vulnerability_Index,  data = AERNASE.clin.hdac9, method = "kruskal.test")

p1 <- ggpubr::ggbarplot(AERNASE.clin.hdac9, 
                  x = "Plaque_Vulnerability_Index",
                  y = "HDAC9", 
                  xlab = "Plaque vulnerability index",
                  ylab = "HDAC9 (normalized expression)\n",
                  col = "Plaque_Vulnerability_Index",
                  fill = "Plaque_Vulnerability_Index",
                  palette = "npg",
                  add = "median_iqr", error.plot = "upper_errorbar") +
  stat_compare_means(label = "p.format",  method = "kruskal.test",
                     label.x = 1, label.y = 50) +
  font("xlab", size = 17) +
  font("ylab", size = 17) +
  font("xy.text", size = 16) +
  font("legend.title", face = "bold") 
  
ggpar(p1, legend = "bottom", legend.title = "Plaque vulnerability index", ylim = c(0, 55))

ggsave(filename = paste0(PLOT_loc, "/", Today, ".",TRAIT_OF_INTEREST,".plaque.PlaqueVulnerabilityIndex.BarPlot.median_iqr.pdf"), plot = last_plot())

compare_means(HDAC9 ~ Plaque_Vulnerability_Index,  data = AERNASE.clin.hdac9, method = "kruskal.test")
```

```{r}
p1 <- ggpubr::ggbarplot(AERNASE.clin.hdac9, 
                  x = "Plaque_Vulnerability_Index",
                  y = "HDAC9", 
                  xlab = "Plaque vulnerability index",
                  ylab = "HDAC9 (normalized expression)\n",
                  col = "Plaque_Vulnerability_Index",
                  fill = "Plaque_Vulnerability_Index",
                  palette = "npg",
                  add = "mean_se", error.plot = "upper_errorbar") +
  stat_compare_means(label = "p.format",  method = "kruskal.test",
                     label.x = 1, label.y = 50) +
  font("xlab", size = 17) +
  font("ylab", size = 17) +
  font("xy.text", size = 16) +
  font("legend.title", face = "bold") 
  
ggpar(p1, legend = "bottom", legend.title = "Plaque vulnerability index", ylim = c(0, 55))

ggsave(filename = paste0(PLOT_loc, "/", Today, ".",TRAIT_OF_INTEREST,".plaque.PlaqueVulnerabilityIndex.BarPlot.means_se.pdf"), plot = last_plot())

```


```{r}
compare_means(HDAC9 ~ Plaque_Vulnerability_Index,  data = subset(AERNASE.clin.hdac9, HDAC9 <100), method = "kruskal.test")

p1 <- ggpubr::ggboxplot(subset(AERNASE.clin.hdac9, HDAC9 <100) , 
                  x = "Plaque_Vulnerability_Index",
                  y = "HDAC9", 
                  xlab = "Plaque vulnerability index",
                  ylab = "HDAC9 (normalized expression)\noutliers above 100 are removed",
                  col = "Plaque_Vulnerability_Index",
                  fill = "Plaque_Vulnerability_Index",
                  palette = "npg",
                  add = "boxplot", error.plot = "crossbar") +
  stat_compare_means(label = "p.format",  method = "kruskal.test",
                     label.x = 1, label.y = 50) +
  font("xlab", size = 17) +
  font("ylab", size = 17) +
  font("xy.text", size = 16) +
  font("legend.title", face = "bold") 
  
ggpar(p1, legend = "bottom", legend.title = "Plaque vulnerability index")

ggsave(filename = paste0(PLOT_loc, "/", Today, ".",TRAIT_OF_INTEREST,".plaque.PlaqueVulnerabilityIndex.Boxplot.outlier_above_100_removed.pdf"), plot = last_plot())

```

```{r}
compare_means(HDAC9 ~ Plaque_Vulnerability_Index,  data = AERNASE.clin.hdac9, method = "kruskal.test")
p1 <- ggpubr::ggboxplot(AERNASE.clin.hdac9, 
                  x = "Plaque_Vulnerability_Index",
                  y = "HDAC9", 
                  facet.by = "Plaque_Vulnerability_Index",
                  xlab = "Plaque vulnerability index",
                  ylab = "HDAC9 (normalized expression)\n",
                  color = "Plaque_Vulnerability_Index",
                  palette = "npg",
                  add = "jitter", 
                  add.params = list(size = 2, jitter = 0.2)) +
  stat_compare_means(label = "p.format",  method = "kruskal.test") +
  font("xlab", size = 17) +
  font("ylab", size = 17) +
  font("xy.text", size = 16) +
  font("legend.title", face = "bold") 
  
ggpar(p1, legend = "bottom", legend.title = "Plaque vulnerability index")

ggsave(filename = paste0(PLOT_loc, "/", Today, ".",TRAIT_OF_INTEREST,".plaque.FacetByPlaqueVulnerabilityIndex.pdf"), plot = last_plot())
```


```{r}
compare_means(HDAC9 ~ Plaque_Vulnerability_Index, group.by = "Gender", data = AERNASE.clin.hdac9, method = "kruskal.test")
p2 <- ggpubr::ggboxplot(AERNASE.clin.hdac9, 
                  x = "Plaque_Vulnerability_Index",
                  y = "HDAC9", 
                  xlab = "Plaque vulnerability index by gender",
                  ylab = "HDAC9 (normalized expression)\n",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") +
  stat_compare_means(aes(group = Gender), label = "p.format",  method = "kruskal.test")
ggpar(p2, legend = "bottom", legend.title = "Plaque vulnerability index")
ggsave(filename = paste0(PLOT_loc, "/", Today, ".",TRAIT_OF_INTEREST,".plaque.PlaqueVulnerabilityIndex.byGender.pdf"), plot = last_plot())

```

```{r}

compare_means(HDAC9 ~ Plaque_Vulnerability_Index, data = AERNASE.clin.hdac9, method = "kruskal.test")
p5 <- ggpubr::ggboxplot(AERNASE.clin.hdac9, 
                  x = "Plaque_Vulnerability_Index",
                  y = "HDAC9", 
                  xlab = "Plaque vulnerability index",
                  ylab = "HDAC9 (normalized expression)\n",
                  color = "Plaque_Vulnerability_Index",
                  palette = "npg",
                  facet.by = "ORyearGroup",
                  add = "jitter") +
  stat_compare_means(label = "p.format",  method = "kruskal.test")
ggpar(p5, legend = "bottom", legend.title = "Plaque vulnerability index")
ggsave(filename = paste0(PLOT_loc, "/", Today, ".",TRAIT_OF_INTEREST,".plaque.PlaqueVulnerabilityIndex_Facet_byYear.pdf"), plot = last_plot())
```

```{r}
compare_means(HDAC9 ~ Plaque_Vulnerability_Index, group.by = "Gender", data = AERNASE.clin.hdac9, method = "kruskal.test")
p6 <- ggpubr::ggboxplot(AERNASE.clin.hdac9, 
                  x = "Plaque_Vulnerability_Index",
                  y = "HDAC9", 
                  xlab = "Plaque vulnerability index",
                  ylab = "HDAC9 (normalized expression)\n",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  facet.by = "ORyearGroup",
                  add = "jitter") +
  stat_compare_means(aes(group = Gender), label = "p.format",  method = "kruskal.test")
ggpar(p6, legend = "bottom", legend.title = "Plaque vulnerability index")
ggsave(filename = paste0(PLOT_loc, "/", Today, ".",TRAIT_OF_INTEREST,".plaque.PlaqueVulnerabilityIndex_Facet_byYear.byGender.pdf"), plot = last_plot())

```



### Model 1

In this model we correct for _Age_, _Gender_, and _year of surgery_.

Here we use the inverse-rank normalized data - visually this is more normally distributed.

Analysis of the plaque vulnerability indez as a function of plaque target(s) levels.

```{r CrossSec: Plaque_Vulnerability_Index - ordinal regression MODEL1 RANK, paged.print=TRUE}
TRAITS.TARGET.RANK.extra = c("HDAC9")

GLM.results <- data.frame(matrix(NA, ncol = 16, nrow = 0))
for (protein in 1:length(TRAITS.TARGET.RANK.extra)) {
  PROTEIN = TRAITS.TARGET.RANK.extra[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  TRAIT = "Plaque_Vulnerability_Index"
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AERNASE.clin.hdac9 %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M1) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.))) %>%
      droplevels(.)
    
    # fix numeric OR year
    # currentDF$ORdate_year <- as.numeric(currentDF$ORdate_year)
    
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    # print(class(currentDF[,TRAIT]))
    # table(currentDF$ORdate_year)
    ### univariate
     # + Hypertension.composite + DiabetesStatus + SmokerCurrent + 
     #            Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
     #            CAD_history + Stroke_history + Peripheral.interv + stenose
    # fit <- polr(currentDF[,TRAIT] ~ currentDF[,PROTEIN] + Age + Gender + ORdate_year, 
    #           data  =  currentDF, 
    #           Hess = TRUE)
    fit <- polr(currentDF[,TRAIT] ~ currentDF[,PROTEIN] + Age + Gender + ORdate_epoch, 
              data  =  currentDF, 
              Hess = TRUE)
    print(summary(fit))
    
    ## store table
    (ctable <- coef(summary(fit)))

    ## calculate and store p values
    p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2
    
    ## combined table
    print((ctable <- cbind(ctable, "p value" = p)))
  }


```

### Model 2

In this model we correct for _Age_, _Gender_, _Hypertension status_, _Diabetes status_, _current smoker status_, _lipid-lowering drugs (LLDs)_, _antiplatelet medication_, _eGFR (MDRD)_, _BMI_, _MedHx_CVD_ (combination of _CAD history_, _stroke history_, and _peripheral interventions_), and _stenosis._.


```{r CrossSec: Plaque_Vulnerability_Index - ordinal regression MODEL2 RANK, paged.print=TRUE}

for (protein in 1:length(TRAITS.TARGET.RANK.extra)) {
  PROTEIN = TRAITS.TARGET.RANK.extra[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  TRAIT = "Plaque_Vulnerability_Index"
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AERNASE.clin.hdac9 %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M2) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.))) %>%
      droplevels(.)
    
    # fix numeric OR year
    # currentDF$ORdate_year <- as.numeric(currentDF$ORdate_year)
    
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    # print(class(currentDF[,TRAIT]))
    ### univariate

    # fit <- polr(as.factor(currentDF[,TRAIT]) ~ currentDF[,PROTEIN] + Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus + SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + MedHx_CVD + stenose,
    #           data  =  currentDF,
    #           Hess = TRUE)
    
    fit <- polr(as.factor(currentDF[,TRAIT]) ~ currentDF[,PROTEIN] + Age + Gender + ORdate_epoch + Hypertension.composite + DiabetesStatus + SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + MedHx_CVD + stenose,
              data  =  currentDF,
              Hess = TRUE)
    
    print(summary(fit))
    
    ## store table
    (ctable <- coef(summary(fit)))

    ## calculate and store p values
    p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2
    
    ## combined table
    print((ctable <- cbind(ctable, "p value" = p)))
  }

```

### Saving data for share

We also want to share the data with our collaborators. And provide some more graphs and summary statistics too.

```{r}
summary(AERNASE.clin.hdac9$HDAC9)
```


```{r}
ggpubr::gghistogram(AERNASE.clin.hdac9, x = "HDAC9",
                    color = "Gender", fill = "Gender",
                    add = "mean", add_density = TRUE,
                    xlab = "HDAC9 (normalized expression)",
                    palette = "npg")
ggsave(filename = paste0(PLOT_loc, "/", Today, ".",TRAIT_OF_INTEREST,".plaque.Histogram.byGender.pdf"), plot = last_plot())

ggpubr::gghistogram(AERNASE.clin.hdac9, x = "HDAC9",
                    color = "Plaque_Vulnerability_Index", fill = "Plaque_Vulnerability_Index",
                    facet.by = "Plaque_Vulnerability_Index",
                    add = "mean", add_density = TRUE,
                    xlab = "HDAC9 (normalized expression)",
                    palette = "npg")
ggsave(filename = paste0(PLOT_loc, "/", Today, ".",TRAIT_OF_INTEREST,".plaque.Histogram.FacetbyPVI.pdf"), plot = last_plot())

ggpubr::gghistogram(AERNASE.clin.hdac9, x = "HDAC9",
                    color = "Plaque_Vulnerability_Index", fill = "Plaque_Vulnerability_Index",
                    # facet.by = "Plaque_Vulnerability_Index",
                    add = "mean", add_density = TRUE,
                    xlab = "HDAC9 (normalized expression)",
                    palette = "npg")
ggsave(filename = paste0(PLOT_loc, "/", Today, ".",TRAIT_OF_INTEREST,".plaque.Histogram.byPVI.pdf"), plot = last_plot())


ggpubr::gghistogram(AERNASE.clin.hdac9, x = "HDAC9",
                    fill = "black", rug = TRUE,
                    add = "mean", add_density = TRUE,
                    xlab = "HDAC9 (normalized expression)",
                    palette = "npg")
ggsave(filename = paste0(PLOT_loc, "/", Today, ".",TRAIT_OF_INTEREST,".plaque.Histogram.pdf"), plot = last_plot())

```

```{r}
AERNASE.clin.hdac9.forSHARE <- subset(AERNASE.clin.hdac9, select = c("STUDY_NUMBER", "Age", "Gender", "HDAC9", "Plaque_Vulnerability_Index"))
```


```{r}
saveRDS(AERNASE.clin.hdac9.forSHARE, file = paste0(OUT_loc, "/", Today, ".",TRAIT_OF_INTEREST,".AERNASE.clin.hdac9.forSHARE.rds"))

fwrite(AERNASE.clin.hdac9.forSHARE, file = paste0(OUT_loc, "/", Today, ".",TRAIT_OF_INTEREST,".AERNASE.clin.hdac9.forSHARE.txt"),
       sep = "\t",
       quote = FALSE,
       na = "NA", 
       verbose = TRUE, showProgress = TRUE, nThread = 8)
```


## Plotting HDAC9 vs Fat 10 perc. in the plaque

```{r}
# Global test
compare_means(HDAC9 ~ Fat.bin_10,  data = AERNASE.clin.hdac9 %>% filter(!is.na(Fat.bin_10)), method = "kruskal.test")
p1 <- ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(Fat.bin_10)), 
                  x = "Fat.bin_10",
                  y = "HDAC9", 
                  xlab = "Fat <10% vs >10%",
                  ylab = "HDAC9 (normalized expression)\n",
                  color = "Fat.bin_10",
                  palette = "npg",
                  add = "jitter") +
  stat_compare_means(label = "p.format",  method = "kruskal.test")
ggpar(p1, legend = "bottom", legend.title = "Fat <10% vs >10%")
ggsave(filename = paste0(PLOT_loc, "/", Today, ".",TRAIT_OF_INTEREST,".plaque.Fat.bin_10.pdf"), plot = last_plot())
```

```{r}
compare_means(HDAC9 ~ Fat.bin_10, group.by = "Gender", data = AERNASE.clin.hdac9 %>% filter(!is.na(Fat.bin_10)), method = "kruskal.test")
p2 <- ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(Fat.bin_10)), 
                  x = "Fat.bin_10",
                  y = "HDAC9", 
                  xlab = "Fat <10% vs >10% by gender",
                  ylab = "HDAC9 (normalized expression)\n",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") +
  stat_compare_means(aes(group = Gender), label = "p.format",  method = "kruskal.test")
ggpar(p2, legend = "bottom", legend.title = "Fat <10% vs >10% by gender")
ggsave(filename = paste0(PLOT_loc, "/", Today, ".",TRAIT_OF_INTEREST,".plaque.Fat.bin_10.byGender.pdf"), plot = last_plot())

```

```{r}
compare_means(HDAC9 ~ Fat.bin_10, data = AERNASE.clin.hdac9 %>% filter(!is.na(Fat.bin_10)), method = "kruskal.test")
p5 <- ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(Fat.bin_10)), 
                  x = "Fat.bin_10",
                  y = "HDAC9", 
                  xlab = "Fat <10% vs >10% by year of surgery",
                  ylab = "HDAC9 (normalized expression)\n",
                  color = "Fat.bin_10",
                  palette = "npg",
                  facet.by = "ORyearGroup",
                  add = "jitter") +
  stat_compare_means(label = "p.format",  method = "kruskal.test")
ggpar(p5, legend = "bottom", legend.title = "Fat <10% vs >10% by year of surgery")
ggsave(filename = paste0(PLOT_loc, "/", Today, ".",TRAIT_OF_INTEREST,".plaque.Fat.bin_10_Facet_byYear.pdf"), plot = last_plot())
```

```{r}
compare_means(HDAC9 ~ Fat.bin_10, group.by = "Gender", data = AERNASE.clin.hdac9 %>% filter(!is.na(Fat.bin_10)), method = "kruskal.test")
p6 <- ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(Fat.bin_10)), 
                  x = "Fat.bin_10",
                  y = "HDAC9", 
                  xlab = "Fat <10% vs >10% by year of surgery and gender",
                  ylab = "HDAC9 (normalized expression)\n",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  facet.by = "ORyearGroup",
                  add = "jitter") +
  stat_compare_means(aes(group = Gender), label = "p.format",  method = "kruskal.test")
ggpar(p6, legend = "bottom", legend.title = "Fat <10% vs >10% by year of surgery and gender")
ggsave(filename = paste0(PLOT_loc, "/", Today, ".",TRAIT_OF_INTEREST,".plaque.Fat.bin_10_Facet_byYear.byGender.pdf"), plot = last_plot())
```

## Plotting HDAC9 vs Fat 40 perc. in the plaque

```{r}
# Global test
compare_means(HDAC9 ~ Fat.bin_40,  data = AERNASE.clin.hdac9 %>% filter(!is.na(Fat.bin_40)), method = "kruskal.test")
p1 <- ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(Fat.bin_40)), 
                  x = "Fat.bin_40",
                  y = "HDAC9", 
                  xlab = "Fat <40% vs >40%",
                  ylab = "HDAC9 (normalized expression)\n",
                  color = "Fat.bin_40",
                  palette = "npg",
                  add = "jitter") +
  stat_compare_means(label = "p.format",  method = "kruskal.test")
ggpar(p1, legend = "bottom", legend.title = "Fat <40% vs >40%")
ggsave(filename = paste0(PLOT_loc, "/", Today, ".",TRAIT_OF_INTEREST,".plaque.Fat.bin_40.pdf"), plot = last_plot())
```

```{r}
compare_means(HDAC9 ~ Fat.bin_40, group.by = "Gender", data = AERNASE.clin.hdac9 %>% filter(!is.na(Fat.bin_40)), method = "kruskal.test")
p2 <- ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(Fat.bin_40)), 
                  x = "Fat.bin_40",
                  y = "HDAC9", 
                  xlab = "Fat <40% vs >40% by gender",
                  ylab = "HDAC9 (normalized expression)\n",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") +
  stat_compare_means(aes(group = Gender), label = "p.format",  method = "kruskal.test")
ggpar(p2, legend = "bottom", legend.title = "Fat <40% vs >40% by gender")
ggsave(filename = paste0(PLOT_loc, "/", Today, ".",TRAIT_OF_INTEREST,".plaque.Fat.bin_40.byGender.pdf"), plot = last_plot())

```

```{r}
compare_means(HDAC9 ~ Fat.bin_40, data = AERNASE.clin.hdac9 %>% filter(!is.na(Fat.bin_40)), method = "kruskal.test")
p5 <- ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(Fat.bin_40)), 
                  x = "Fat.bin_40",
                  y = "HDAC9", 
                  xlab = "Fat <40% vs >40% by year of surgery",
                  ylab = "HDAC9 (normalized expression)\n",
                  color = "Fat.bin_40",
                  palette = "npg",
                  facet.by = "ORyearGroup",
                  add = "jitter") +
  stat_compare_means(label = "p.format",  method = "kruskal.test")
ggpar(p5, legend = "bottom", legend.title = "Fat <40% vs >40% by year of surgery")
ggsave(filename = paste0(PLOT_loc, "/", Today, ".",TRAIT_OF_INTEREST,".plaque.Fat.bin_40_Facet_byYear.pdf"), plot = last_plot())
```

```{r}
compare_means(HDAC9 ~ Fat.bin_40, group.by = "Gender", data = AERNASE.clin.hdac9 %>% filter(!is.na(Fat.bin_40)), method = "kruskal.test")
p6 <- ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(Fat.bin_40)), 
                  x = "Fat.bin_40",
                  y = "HDAC9", 
                  xlab = "Fat <40% vs >40% by year of surgery and gender",
                  ylab = "HDAC9 (normalized expression)\n",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  facet.by = "ORyearGroup",
                  add = "jitter") +
  stat_compare_means(aes(group = Gender), label = "p.format",  method = "kruskal.test")
ggpar(p6, legend = "bottom", legend.title = "Fat <40% vs >40% by year of surgery and gender")
ggsave(filename = paste0(PLOT_loc, "/", Today, ".",TRAIT_OF_INTEREST,".plaque.Fat.bin_40_Facet_byYear.byGender.pdf"), plot = last_plot())
```


## Plotting HDAC9 vs IPH in the plaque

```{r}
# Global test
compare_means(HDAC9 ~ IPH.bin,  data = AERNASE.clin.hdac9 %>% filter(!is.na(IPH.bin)), method = "kruskal.test")
p1 <- ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(IPH.bin)), 
                  x = "IPH.bin",
                  y = "HDAC9", 
                  xlab = "Intraplaque hemorrhage (no vs. yes)",
                  ylab = "HDAC9 (normalized expression)\n",
                  color = "IPH.bin",
                  palette = "npg",
                  add = "jitter") +
  stat_compare_means(label = "p.format",  method = "kruskal.test")
ggpar(p1, legend = "bottom", legend.title = "IPH")
ggsave(filename = paste0(PLOT_loc, "/", Today, ".",TRAIT_OF_INTEREST,".plaque.IPH.bin.pdf"), plot = last_plot())
```

```{r}
compare_means(HDAC9 ~ IPH.bin, group.by = "Gender", data = AERNASE.clin.hdac9 %>% filter(!is.na(IPH.bin)), method = "kruskal.test")
p2 <- ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(IPH.bin)), 
                  x = "IPH.bin",
                  y = "HDAC9", 
                  xlab = "Intraplaque hemorrhage (no vs. yes) by gender",
                  ylab = "HDAC9 (normalized expression)\n",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") +
  stat_compare_means(aes(group = Gender), label = "p.format",  method = "kruskal.test")
ggpar(p2, legend = "bottom", legend.title = "IPH by gender")
ggsave(filename = paste0(PLOT_loc, "/", Today, ".",TRAIT_OF_INTEREST,".plaque.IPH.bin.byGender.pdf"), plot = last_plot())

```

```{r}
compare_means(HDAC9 ~ IPH.bin, data = AERNASE.clin.hdac9 %>% filter(!is.na(IPH.bin)), method = "kruskal.test")
p5 <- ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(IPH.bin)), 
                  x = "IPH.bin",
                  y = "HDAC9", 
                  xlab = "Intraplaque hemorrhage (no vs. yes) by year of surgery",
                  ylab = "HDAC9 (normalized expression)\n",
                  color = "IPH.bin",
                  palette = "npg",
                  facet.by = "ORyearGroup",
                  add = "jitter") +
  stat_compare_means(label = "p.format",  method = "kruskal.test")
ggpar(p5, legend = "bottom", legend.title = "IPH by year of surgery")
ggsave(filename = paste0(PLOT_loc, "/", Today, ".",TRAIT_OF_INTEREST,".plaque.IPH.bin_Facet_byYear.pdf"), plot = last_plot())
```

```{r}
compare_means(HDAC9 ~ IPH.bin, group.by = "Gender", data = AERNASE.clin.hdac9 %>% filter(!is.na(IPH.bin)), method = "kruskal.test")
p6 <- ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(IPH.bin)), 
                  x = "IPH.bin",
                  y = "HDAC9", 
                  xlab = "Intraplaque hemorrhage (no vs. yes) by year of surgery and gender",
                  ylab = "HDAC9 (normalized expression)\n",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  facet.by = "ORyearGroup",
                  add = "jitter") +
  stat_compare_means(aes(group = Gender), label = "p.format",  method = "kruskal.test")
ggpar(p6, legend = "bottom", legend.title = "IPH by year of surgery and gender")
ggsave(filename = paste0(PLOT_loc, "/", Today, ".",TRAIT_OF_INTEREST,".plaque.IPH.bin_Facet_byYear.byGender.pdf"), plot = last_plot())
```

## Plotting HDAC9 vs Calcification in the plaque

```{r}
# Global test
compare_means(HDAC9 ~ Calc.bin,  data = AERNASE.clin.hdac9 %>% filter(!is.na(Calc.bin)), method = "kruskal.test")
p1 <- ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(Calc.bin)), 
                  x = "Calc.bin",
                  y = "HDAC9", 
                  xlab = "Calcification (no/minor vs. moderate/heavy)",
                  ylab = "HDAC9 (normalized expression)\n",
                  color = "Calc.bin",
                  palette = "npg",
                  add = "jitter") +
  stat_compare_means(label = "p.format",  method = "kruskal.test")
ggpar(p1, legend = "bottom", legend.title = "Calcification")
ggsave(filename = paste0(PLOT_loc, "/", Today, ".",TRAIT_OF_INTEREST,".plaque.Calc.bin.pdf"), plot = last_plot())
```

```{r}
compare_means(HDAC9 ~ Calc.bin, group.by = "Gender", data = AERNASE.clin.hdac9 %>% filter(!is.na(Calc.bin)), method = "kruskal.test")
p2 <- ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(Calc.bin)), 
                  x = "Calc.bin",
                  y = "HDAC9", 
                  xlab = "Calcification (no/minor vs. moderate/heavy) by gender",
                  ylab = "HDAC9 (normalized expression)\n",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") +
  stat_compare_means(aes(group = Gender), label = "p.format",  method = "kruskal.test")
ggpar(p2, legend = "bottom", legend.title = "Calcification by gender")
ggsave(filename = paste0(PLOT_loc, "/", Today, ".",TRAIT_OF_INTEREST,".plaque.Calc.bin.byGender.pdf"), plot = last_plot())

```

```{r}
compare_means(HDAC9 ~ Calc.bin, data = AERNASE.clin.hdac9 %>% filter(!is.na(Calc.bin)), method = "kruskal.test")
p5 <- ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(Calc.bin)), 
                  x = "Calc.bin",
                  y = "HDAC9", 
                  xlab = "Calcification (no/minor vs. moderate/heavy) by year of surgery",
                  ylab = "HDAC9 (normalized expression)\n",
                  color = "Calc.bin",
                  palette = "npg",
                  facet.by = "ORyearGroup",
                  add = "jitter") +
  stat_compare_means(label = "p.format",  method = "kruskal.test")
ggpar(p5, legend = "bottom", legend.title = "Calcification by year of surgery")
ggsave(filename = paste0(PLOT_loc, "/", Today, ".",TRAIT_OF_INTEREST,".plaque.Calc.bin_Facet_byYear.pdf"), plot = last_plot())
```

```{r}
compare_means(HDAC9 ~ Calc.bin, group.by = "Gender", data = AERNASE.clin.hdac9 %>% filter(!is.na(Calc.bin)), method = "kruskal.test")
p6 <- ggpubr::ggboxplot(AERNASE.clin.hdac9 %>% filter(!is.na(Calc.bin)), 
                  x = "Calc.bin",
                  y = "HDAC9", 
                  xlab = "Calcification (no/minor vs. moderate/heavy) by year of surgery and gender",
                  ylab = "HDAC9 (normalized expression)\n",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  facet.by = "ORyearGroup",
                  add = "jitter") +
  stat_compare_means(aes(group = Gender), label = "p.format",  method = "kruskal.test")
ggpar(p6, legend = "bottom", legend.title = "Calcification by year of surgery and gender")
ggsave(filename = paste0(PLOT_loc, "/", Today, ".",TRAIT_OF_INTEREST,".plaque.Calc.bin_Facet_byYear.byGender.pdf"), plot = last_plot())
```



# Session information

--------------------------------------------------------------------------------

    Version:      v1.0.5
    Last update:  2023-05-31
    Written by:   Sander W. van der Laan (s.w.vanderlaan-2[at]umcutrecht.nl).
    Description:  Script to analyse HDAC9 from the Ather-Express Biobank Study.
    Minimum requirements: R version 3.5.2 (2018-12-20) -- 'Eggshell Igloo', macOS Mojave (10.14.2).
    
    **MoSCoW To-Do List**
    The things we Must, Should, Could, and Would have given the time we have.
    _M_

    _S_

    _C_

    _W_

    **Changes log**
    * v1.0.5 Fixed forest plot and alternative boxplot for symptoms.
    * v1.0.4 Made histogram of PVI. Exported HDAC9 and PVI data.
    * v1.0.3 Small adaptations to PVI-plots.
    * v1.0.2 Changed the PVI-plot.
    * v1.0.1 Added figures on fat in the plaque.
    * v1.0.0 Inital version.
    

--------------------------------------------------------------------------------

```{r eval = TRUE}
sessionInfo()
```

# Saving environment
```{r Saving}
save.image(paste0(PROJECT_loc, "/",Today,".",PROJECTNAME,".bulkRNAseq.additional_figures.RData"))
```

+-----------------------------------------------------------------------------------------------------------------------------------------+
| <sup>© 1979-2023 Sander W. van der Laan | s.w.vanderlaan[at]gmail.com | [vanderlaan.science](https://vanderlaan.science).</sup> |
+-----------------------------------------------------------------------------------------------------------------------------------------+


